Final Government Distribution Chapter11 IPCC AR6 WGI
1 Table Of Content
2
3 Executive Summary ................................................................................................................................... 6
4
5 11.1 Framing ........................................................................................................................................11
6 11.1.1 Introduction to the chapter ...................................................................................................... 11
7 11.1.2 What are extreme events and how are their changes studied? .................................................. 11
8 11.1.3 Types of extremes assessed in this chapter .............................................................................. 12
9 11.1.4 Effects of greenhouse gas and other external forcings on extremes .......................................... 13
10
11 BOX 11.1: Thermodynamic and dynamic changes in extremes across scales ....................................15
12
13 11.1.5 Effects of large-scale circulation on changes in extremes ........................................................ 17
14 11.1.6 Effects of regional-scale processes and forcings and feedbacks on changes in extremes........... 18
15 11.1.7 Global-scale synthesis............................................................................................................. 19
16
17 BOX 11.2: Low-likelihood high-impact changes in extremes ..................................................................24
18
19 11.2 Data and Methods ........................................................................................................................26
20 11.2.1 Definition of extremes ............................................................................................................ 26
21 11.2.2 Data........................................................................................................................................ 27
22
23 BOX 11.3: Extremes in paleoclimate archives compared to instrumental records.................................28
24
25 11.2.3 Attribution of extremes ........................................................................................................... 31
26 11.2.4 Projecting changes in extremes as a function of global warming levels .................................... 32
27
28 Cross-Chapter Box 11.1: Translating between regional information at global warming levels vs
29 scenarios for end users .......................................................................................................................34
30
31 11.3 Temperature extremes .................................................................................................................38
32 11.3.1 Mechanisms and drivers ......................................................................................................... 38
33 11.3.2 Observed trends ...................................................................................................................... 40
34 11.3.3 Model evaluation .................................................................................................................... 43
35 11.3.4 Detection and attribution, event attribution.............................................................................. 45
36 11.3.5 Projections .............................................................................................................................. 47
37
38 11.4 Heavy precipitation ......................................................................................................................51
39 11.4.1 Mechanisms and drivers ......................................................................................................... 51
40 11.4.2 Observed Trends ..................................................................................................................... 52
41 11.4.3 Model evaluation .................................................................................................................... 56
42 11.4.4 Detection and attribution, event attribution.............................................................................. 57
43 11.4.5 Projections .............................................................................................................................. 59
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1
2 11.5 Floods............................................................................................................................................63
3 11.5.1 Mechanisms and drivers ......................................................................................................... 64
4 11.5.2 Observed trends ...................................................................................................................... 65
5 11.5.3 Model evaluation .................................................................................................................... 65
6 11.5.4 Attribution .............................................................................................................................. 66
7 11.5.5 Future projections ................................................................................................................... 67
8
9 11.6 Droughts .......................................................................................................................................68
10 11.6.1 Mechanisms and drivers ......................................................................................................... 68
11 11.6.1.1 Precipitation deficits ........................................................................................................... 69
12 11.6.1.2 Atmospheric evaporative demand ....................................................................................... 69
13 11.6.1.3 Soil moisture deficits .......................................................................................................... 70
14 11.6.1.4 Hydrological deficits........................................................................................................... 71
15 11.6.1.5 Atmospheric-based drought indices ..................................................................................... 71
16 11.6.1.6 Relation of assessed variables and metrics for changes in different drought types ................ 71
17 11.6.2 Observed trends ...................................................................................................................... 72
18 11.6.2.1 Precipitation deficits ........................................................................................................... 72
19 11.6.2.2 Atmospheric evaporative demand ....................................................................................... 72
20 11.6.2.3 Soil moisture deficits .......................................................................................................... 73
21 11.6.2.4 Hydrological deficits........................................................................................................... 74
22 11.6.2.5 Atmospheric-based drought indices ..................................................................................... 74
23 11.6.2.6 Synthesis for different drought types ................................................................................... 75
24 11.6.3 Model evaluation .................................................................................................................... 75
25 11.6.3.1 Precipitation deficits ........................................................................................................... 75
26 11.6.3.2 Atmospheric evaporative demand ....................................................................................... 75
27 11.6.3.3 Soil moisture deficits .......................................................................................................... 76
28 11.6.3.4 Hydrological deficits........................................................................................................... 77
29 11.6.3.5 Atmospheric-based drought indices ..................................................................................... 77
30 11.6.3.6 Synthesis for different drought types ................................................................................... 77
31 11.6.4 Detection and attribution, event attribution.............................................................................. 78
32 11.6.4.1 Precipitation deficits ........................................................................................................... 78
33 11.6.4.2 Soil moisture deficits .......................................................................................................... 79
34 11.6.4.3 Hydrological deficits........................................................................................................... 79
35 11.6.4.4 Atmospheric-based drought indices ..................................................................................... 80
36 11.6.4.5 Synthesis for different drought types ................................................................................... 80
37 11.6.5 Projections .............................................................................................................................. 81
38 11.6.5.1 Precipitation deficits ........................................................................................................... 81
39 11.6.5.2 Atmospheric evaporative demand ....................................................................................... 82
40 11.6.5.3 Soil moisture deficits .......................................................................................................... 83
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1 11.6.5.4 Hydrological deficits........................................................................................................... 84
2 11.6.5.5 Atmospheric-based drought indices ..................................................................................... 85
3 11.6.5.6 Synthesis for different drought types ................................................................................... 86
4
5 11.7 Extreme storms ............................................................................................................................87
6 11.7.1 Tropical cyclones.................................................................................................................... 88
7 11.7.1.1 Mechanisms and drivers...................................................................................................... 88
8 11.7.1.2 Observed trends .................................................................................................................. 88
9 11.7.1.3 Model evaluation ................................................................................................................ 90
10 11.7.1.4 Detection and attribution, event attribution .......................................................................... 92
11 11.7.1.5 Projections .......................................................................................................................... 94
12 11.7.2 Extratropical storms ................................................................................................................ 97
13 11.7.2.1 Observed trends .................................................................................................................. 97
14 11.7.2.2 Model evaluation ................................................................................................................ 98
15 11.7.2.3 Detection and attribution, event attribution .......................................................................... 98
16 11.7.2.4 Projections .......................................................................................................................... 98
17 11.7.3 Severe convective storms ........................................................................................................ 99
18 11.7.3.1 Mechanisms and drivers.................................................................................................... 100
19 11.7.3.2 Observed trends ................................................................................................................ 101
20 11.7.3.3 Model evaluation .............................................................................................................. 102
21 11.7.3.4 Detection and attribution, event attribution ........................................................................ 103
22 11.7.3.5 Projections ........................................................................................................................ 103
23 11.7.4 Extreme winds ...................................................................................................................... 104
24
25 11.8 Compound events .......................................................................................................................106
26 11.8.1 Overview .............................................................................................................................. 106
27 11.8.2 Concurrent extremes in coastal and estuarine regions ............................................................ 107
28 11.8.3 Concurrent droughts and heat waves ..................................................................................... 108
29
30 BOX 11.4: Case study: Global-scale concurrent climate anomalies at the example of the 2015-2016
31 extreme El Niño and the 2018 boreal spring/summer extremes ............................................................109
32
33 11.9 Regional information on extremes .............................................................................................113
34 11.9.1 Overview .............................................................................................................................. 113
35 11.9.2 Temperature extremes........................................................................................................... 114
36 11.9.3 Heavy precipitation............................................................................................................... 114
37 11.9.4 Droughts............................................................................................................................... 115
38
39 Frequently Asked Questions ...................................................................................................................117
40 FAQ 11.1: How do changes in climate extremes compare with changes in climate averages? .......... 117
41 FAQ 11.2: Will unprecedented extremes occur as a result of human-induced climate change? ........ 119
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1 FAQ 11.3: Did climate change cause that recent extreme event in my country?............................... 120
2
3 Large tables ..........................................................................................................................................122
4
5 Acknowledgements .................................................................................................................................233
6
7 References ..........................................................................................................................................234
8
9 Appendix 11.A ........................................................................................................................................314
10
11 Figures ..........................................................................................................................................316
12
13
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1 Executive Summary
2
3 This chapter assesses changes in weather and climate extremes on regional and global scales, including
4 observed changes and their attribution, as well as projected changes. The extremes considered include
5 temperature extremes, heavy precipitation and pluvial floods, river floods, droughts, storms (including
6 tropical cyclones), as well as compound events (multivariate and concurrent extremes). Changes in marine
7 extremes are addressed in Chapter 9 and Cross-Chapter Box 9.1. Assessments of past changes and their
8 drivers are from 1950 onward, unless indicated otherwise. Projections for changes in extremes are presented
9 for different levels of global warming, supplemented with information for the conversion to emission
10 scenario-based projections (Cross-Chapter Box 11.1; Chapter 4, Table 4.2). Since AR5, there have been
11 important new developments and knowledge advances on changes in weather and climate extremes, in
12 particular regarding human influence on individual extreme events, on changes in droughts, tropical
13 cyclones, and compound events, and on projections at different global warming levels (1.5°C–4°C). These,
14 together with new evidence at regional scales, provide a stronger basis and more regional information for the
15 AR6 assessment on weather and climate extremes.
16
17 It is an established fact that human-induced greenhouse gas emissions have led to an increased
18 frequency and/or intensity of some weather and climate extremes since pre-industrial time, in
19 particular for temperature extremes. Evidence of observed changes in extremes and their attribution to
20 human influence (including greenhouse gas and aerosol emissions and land-use changes) has strengthened
21 since AR5, in particular for extreme precipitation, droughts, tropical cyclones and compound extremes
22 (including dry/hot events and fire weather). Some recent hot extreme events would have been extremely
23 unlikely to occur without human influence on the climate system. {11.2, 11.3, 11.4, 11.6, 11.7, 11.8}
24
25 Regional changes in the intensity and frequency of climate extremes generally scale with global
26 warming. New evidence strengthens the conclusion from SR1.5 that even relatively small incremental
27 increases in global warming (+0.5°C) cause statistically significant changes in extremes on the global
28 scale and for large regions (high confidence). In particular, this is the case for temperature extremes
29 (very likely), the intensification of heavy precipitation (high confidence) including that associated with
30 tropical cyclones (medium confidence), and the worsening of droughts in some regions (high
31 confidence). The occurrence of extreme events unprecedented in the observed record will increase with
32 increasing global warming, even at 1.5°C of global warming. Projected percentage changes in frequency are
33 higher for the rarer extreme events (high confidence). {11.1, 11.2, 11.3, 11.4, 11.6, 11.9, CC-Box 11.1}
34
35 Methods and Data for Extremes
36
37 Since AR5, the confidence about past and future changes in weather and climate extremes has
38 increased due to better physical understanding of processes, an increasing proportion of the scientific
39 literature combining different lines of evidence, and improved accessibility to different types of climate
40 models (high confidence). There have been improvements in some observation-based datasets,
41 including reanalysis data (high confidence). Climate models can reproduce the sign of changes in
42 temperature extremes observed globally and in most regions, although the magnitude of the trends
43 may differ (high confidence). Models are able to capture the large-scale spatial distribution of precipitation
44 extremes over land (high confidence). The intensity and frequency of extreme precipitation simulated by
45 Coupled Model Intercomparison Project Phase 6 (CMIP6) models are similar to those simulated by CMIP5
46 models (high confidence). Higher horizontal model resolution improves the spatial representation of some
47 extreme events (e.g., heavy precipitation events), in particular in regions with highly varying topography
48 (high confidence). {11.2, 11.3, 11.4}
49
50 Temperature Extremes
51
52 The frequency and intensity of hot extremes have increased and those of cold extremes have decreased
53 on the global scale since 1950 (virtually certain). This also applies at regional scale, with more than
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1 80% of AR6 regions 1 showing similar changes assessed to be at least likely. In a few regions, limited
2 evidence (data or literature) prevents the reliable estimation of trends. {11.3, 11.9}
3
4 Human-induced greenhouse gas forcing is the main driver of the observed changes in hot and cold
5 extremes on the global scale (virtually certain) and on most continents (very likely). The effect of
6 enhanced greenhouse gas concentrations on extreme temperatures is moderated or amplified at the regional
7 scale by regional processes such as soil moisture or snow/ice-albedo feedbacks, by regional forcing from
8 land use and land-cover changes, or aerosol concentrations, and decadal and multidecadal natural variability.
9 Changes in anthropogenic aerosol concentrations have likely affected trends in hot extremes in some regions.
10 Irrigation and crop expansion have attenuated increases in summer hot extremes in some regions, such as the
11 U.S. Midwest (medium confidence). Urbanization has likely exacerbated changes in temperature extremes in
12 cities, in particular for night-time extremes. {11.1, 11.2, 11.3}
13
14 The frequency and intensity of hot extremes will continue to increase and those of cold extremes will
15 continue to decrease, at both global and continental scales and in nearly all inhabited regions1 with
16 increasing global warming levels. This will be the case even if global warming is stabilized at 1.5°C.
17 Relative to present-day conditions, changes in the intensity of extremes would be at least double at 2°C, and
18 quadruple at 3°C of global warming, compared to changes at 1.5°C of global warming. The number of hot
19 days and hot nights and the length, frequency, and/or intensity of warm spells or heat waves will increase
20 over most land areas (virtually certain). In most regions, future changes in the intensity of temperature
21 extremes will very likely be proportional to changes in global warming, and up to 2–3 times larger (high
22 confidence). The highest increase of temperature of hottest days is projected in some mid-latitude and semi-
23 arid regions, at about 1.5 time to twice the rate of global warming (high confidence). The highest increase of
24 temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming
25 (high confidence). The frequency of hot temperature extreme events will very likely increase non-linearly
26 with increasing global warming, with larger percentage increases for rarer events. {11.2, 11.3, 11.9; Table
27 11.1; Figure 11.3}
28
29 Heavy Precipitation and Pluvial Floods
30
31 The frequency and intensity of heavy precipitation events have likely increased at the global scale over
32 a majority of land regions with good observational coverage. Heavy precipitation has likely increased
33 on the continental scale over three continents: North America, Europe, and Asia. Regional increases in
34 the frequency and/or intensity of heavy precipitation have been observed with at least medium confidence for
35 nearly half of AR6 regions, including WSAF, ESAF, WSB, SAS, ESB, REF, WCA, ECA, TIB, EAS, SEA,
36 NAU, NEU, EEU, GIC, WCE, SES, CNA, and ENA. {11.4, 11.9}
37
38 Human influence, in particular greenhouse gas emissions, is likely the main driver of the observed
39 global scale intensification of heavy precipitation in land regions. It is likely that human-induced climate
40 change has contributed to the observed intensification of heavy precipitation at the continental scale in North
41 America, Europe and Asia. Evidence of a human influence on heavy precipitation has emerged in some
42 regions. {11.4, 11.9, Table 11.1}
43
44 Heavy precipitation will generally become more frequent and more intense with additional global
45 warming. At global warming levels of 4°C relative to the pre-industrial, very rare (e.g., 1 in 10 or more
1
See Figure 1.18 in Chapter 1 for definition of AR6 regions. Acronyms for inhabited regions: ARP: Arabian Peninsula;
CAF: C. Africa; CAR : Caribbean; CAU: C. Australia; CNA: C. North America; EAS: E. Asia; EAU: E. Australia;
ECA: E. Central Asia; EEU: E. Europe; ENA: E. North America; ESAF: E. Southern Africa; ESB: E. Siberia; GIC:
Greenland/Iceland; MDG: Madagascar; MED: Mediterranean; NAU: N. Australia; NCA: N. Central America; NEAF:
N.E. Africa; NEN: N.E. North America; NES: N.E. South America; NEU: N. Europe; NSA: N. South America; NWN:
N.W. North America; NWS: N.W. South America; NZ: New Zealand; RAR: Russian Arctic; RFE: Russian Far East;
SAH: Sahara; SAM: South American Monsoon; SAS: South Asia; SAU: Southern Australia; SCA: S. Central America;
SEAF: S.E. Africa; SES: S.E. South America; SSA: S. South America; SWS: S.W. South America; TIB: Tibetan
Plateau; WAF: Western Africa; WCA: W. Central Asia; WCE: Western & Central Europe; WNA: W. North America;
WSAF: W. Southern Africa; WSB: W. Siberia.
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1 years) heavy precipitation events would become more frequent and more intense than in the recent
2 past, on the global scale (virtually certain) and in all continents and AR6 regions. The increase in
3 frequency and intensity is extremely likely for most continents and very likely for most AR6 regions. At
4 the global scale, the intensification of heavy precipitation will follow the rate of increase in the maximum
5 amount of moisture that the atmosphere can hold as it warms (high confidence), of about 7% per 1°C of
6 global warming. The increase in the frequency of heavy precipitation events will accelerate with more
7 warming and will be higher for rarer events (high confidence), with a likely doubling and tripling in the
8 frequency of 10-year and 50-year events, respectively, compared to the recent past at 4°C of global warming.
9 Increases in the intensity of extreme precipitation at regional scales will vary, depending on the amount of
10 regional warming, changes in atmospheric circulation and storm dynamics (high confidence). {11.4, Box
11 11.1}
12
13 The projected increase in the intensity of extreme precipitation translates to an increase in the
14 frequency and magnitude of pluvial floods – surface water and flash floods – (high confidence), as
15 pluvial flooding results from precipitation intensity exceeding the capacity of natural and artificial
16 drainage systems. {11.4}
17
18 River Floods
19
20 Significant trends in peak streamflow have been observed in some regions over the past decades (high
21 confidence). This includes increases in RAR, NSA, and parts of SES, NEU, ENA and
22 decreases in NES, SAU, and parts of MED and EAS). The seasonality of river floods has changed in cold
23 regions where snow-melt is involved, with an earlier occurrence of peak streamflow (high confidence).
24 {11.5}
25
26 Global hydrological models project a larger fraction of land areas to be affected by an increase in
27 river floods than by a decrease in river floods (medium confidence). River floods are projected to become
28 more frequent and intense in some AR6 regions (RAR, SEA, SAS, NWS) (high confidence) and less
29 frequent and intense in others (WCE, EEU, MED) (high confidence). Regional changes in river floods are
30 more uncertain than changes in pluvial floods because complex hydrological processes and forcings,
31 including land cover change and human water management, are involved. {11.5}
32
33 Droughts
34
35 Different drought types exist, and they are associated with different impacts and respond differently to
36 increasing greenhouse gas concentrations. Precipitation deficits and changes in evapotranspiration (ET)
37 govern net water availability. A lack of sufficient soil moisture, sometimes amplified by increased
38 atmospheric evaporative demand (AED), results in agricultural and ecological drought. Lack of runoff and
39 surface water result in hydrological drought. {11.6}
40
41 Human-induced climate change has contributed to decreases in water availability during the dry
42 season over a predominant fraction of the land area due to evapotranspiration increases (medium
43 confidence). Increases in evapotranspiration have been driven by AED increases induced by increased
44 temperature, decreased relative humidity and increased net radiation (high confidence). Trends in
45 precipitation are not a main driver in affecting global-scale trends in drought (medium confidence), but have
46 induced drying trends in a few AR6 regions (NES: high confidence; WAF, CAF, ESAF, SAM, SWS, SSA,
47 SAS: medium confidence). Increasing trends in agricultural and ecological droughts have been observed on
48 all continents (WAF, CAF, WSAF, ESAF, WCA, ECA, EAS, SAU, MED, WCE, WNA, NES: medium
49 confidence), but decreases only in one AR6 region (NAU: medium confidence). Increasing trends in
50 hydrological droughts have been observed in a few AR6 regions (MED: high confidence; WAF, EAS, SAU:
51 medium confidence). Regional-scale attribution shows that human-induced climate change has contributed to
52 increased agricultural and ecological droughts (MED, WNA), and increased hydrological drought (MED) in
53 some regions (medium confidence). {11.6, 11.9}
54
55 The land area affected by increasing drought frequency and severity expands with increasing global
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1 warming (high confidence). Several regions will be affected by more severe agricultural and ecological
2 droughts even if global warming is stabilized in a range of 1.5°C-2°C of global warming (high confidence),
3 including WCE, MED, EAU, SAU, SCA, NSA, SAM, SWS, SSA, NCA, CAN, WSAF, ESAF and MDG
4 (medium confidence). At 4°C of global warming, about 50% of all inhabited AR6 regions would be affected
5 (WCE, MED, CAU, EAU, SAU, WCA, EAS, SCA, CAR, NSA, NES, SAM, SWS, SSA, NCA, CAN, ENA,
6 WNA, WSAF, ESAF, MDG; medium confidence or higher), and only two regions (NEAF, SAS) would
7 experience decreases in agricultural and ecological drought (medium confidence). There is high confidence
8 that the projected increases in agricultural and ecological droughts are strongly affected by ET increases
9 associated with enhanced AED. Several regions are projected to be more strongly affected by hydrological
10 droughts with increasing global warming (at 4°C of global warming: NEU, WCE, EEU, MED, SAU, WCA,
11 SCA, NSA, SAM, SWS, SSA, WNA, WSAF, ESAF, MDG; medium confidence or higher). There is low
12 confidence that effects of enhanced atmospheric CO2 concentrations on plant water-use efficiency alleviate
13 extreme agricultural and ecological droughts in conditions characterized by limited soil moisture
14 and enhanced AED. There is also low confidence that these effects will substantially reduce global plant
15 transpiration and the severity of hydrological droughts. There is high confidence that the land carbon sink
16 will become less efficient due to soil moisture limitations and associated drought conditions in some regions
17 in higher-emission scenarios, in particular under global warming levels above 4°C. {11.6, 11.9, CC-Box 5.1}
18
19 Extreme Storms, Including Tropical Cyclones (TCs)
20
21 The average and maximum rain rates associated with TCs, extratropical cyclones and atmospheric
22 rivers across the globe, and severe convective storms in some regions, increase in a warming world
23 (high confidence). Available event attribution studies of observed strong TCs provide medium confidence
24 for a human contribution to extreme TC rainfall. Peak TC rain rates increase with local warming at least at
25 the rate of mean water vapour increase over oceans (about 7% per 1°C of warming) and in some cases
26 exceeding this rate due to increased low-level moisture convergence caused by increases in TC wind
27 intensity (medium confidence). {11.7, 11.4, Box 11.1}
28
29 It is likely that the global proportion of major TC (Category 3–5) intensities over the past four decades
30 has increased. The average location where TCs reach their peak wind intensity has very likely migrated
31 poleward in the western North Pacific Ocean since the 1940s, and TC translation speed has likely slowed
32 over the conterminous USA since 1900. Evidence of similar trends in other regions is not robust. The global
33 frequency of TC rapid intensification events has likely increased over the past four decades. None of these
34 changes can be explained by natural variability alone (medium confidence).
35
36 The proportion of intense TCs, average peak TC wind speeds, and peak wind speeds of the most
37 intense TCs will increase on the global scale with increasing global warming (high confidence). The
38 total global frequency of TC formation will decrease or remain unchanged with increasing global warming
39 (medium confidence). {11.7.1}
40
41 There is low confidence in past changes of maximum wind speeds and other measures of dynamical
42 intensity of extratropical cyclones. Future wind speed changes are expected to be small, although
43 poleward shifts in the storm tracks could lead to substantial changes in extreme wind speeds in some
44 regions (medium confidence). There is low confidence in past trends in characteristics of severe convective
45 storms, such as hail and severe winds, beyond an increase in precipitation rates. The frequency of springtime
46 severe convective storms is projected to increase in the USA, leading to a lengthening of the severe
47 convective storm season (medium confidence); evidence in other regions is limited. {11.7.2, 11.7.3}.
48
49 Compound Events, Including Dry/Hot events, Fire Weather, Compound Flooding, and Concurrent
50 Extremes
51
52 The probability of compound events has likely increased in the past due to human-induced climate
53 change and will likely continue to increase with further global warming. Concurrent heat waves and
54 droughts have become more frequent and this trend will continue with higher global warming (high
55 confidence). Fire weather conditions (compound hot, dry and windy events) have become more probable in
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1 some regions (medium confidence) and there is high confidence that they will become more frequent in some
2 regions at higher levels of global warming. The probability of compound flooding (storm surge, extreme
3 rainfall and/or river flow) has increased in some locations, and will continue to increase due to both sea level
4 rise and increases in heavy precipitation, including changes in precipitation intensity associated with TCs
5 (high confidence). The land area affected by concurrent extremes has increased (high confidence).
6 Concurrent extreme events at different locations, but possibly affecting similar sectors (e.g., critical crop-
7 producing areas for global food supply) in different regions, will become more frequent with increasing
8 global warming, in particular above 2°C of global warming (high confidence). {11.8, Box 11.3, Box 11.4}.
9
10 Low-Likelihood High-Impact (LLHI) Events Associated With Climate Extremes
11
12 The future occurrence of LLHI events linked to climate extremes is generally associated with low
13 confidence, but cannot be excluded, especially at global warming levels above 4°C. Compound events,
14 including concurrent extremes, are a factor increasing the probability of LLHI events (high confidence).
15 With increasing global warming some compound events with low likelihood in past and current climate will
16 become more frequent, and there is a higher chance of occurrence of historically unprecedented events and
17 surprises (high confidence). However, even extreme events that do not have a particularly low probability in
18 the present climate (at more than 1°C of global warming) can be perceived as surprises because of the pace
19 of global warming (high confidence). {Box 11.2}
20
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1 11.1 Framing
2
3 11.1.1 Introduction to the chapter
4
5 This chapter provides assessments of changes in weather and climate extremes (collectively referred to as
6 extremes) framed in terms of the relevance to the Working Group II assessment. It assesses observed
7 changes in extremes, their attribution to causes, and future projections, at three global warming levels: 1.5°C,
8 2°C, 4°C. This chapter is also one of the four “regional chapters” of the WGI report (along with Chapters 10
9 and 12 and the Atlas). Consequently, while it encompasses assessments of changes in extremes at global and
10 continental scales to provide a large-scale context, it also addresses changes in extremes at regional scales.
11
12 Extremes are climatic impact-drivers (Annex VII: Glossary, see Chapter 12 for a comprehensive
13 assessment). The IPCC risk framework (Chapter 1) articulates clearly that the exposure and vulnerability to
14 climatic impact-drivers, such as extremes, modulate the risk of adverse impacts of these drivers, and that
15 adaptation that reduces exposure and vulnerability will increase resilience resulting in a reduction in impacts.
16 Nonetheless, changes in extremes lead to changes in impacts not only as a direct consequence of changes in
17 their magnitude and frequency, but also through their influence on exposure and resilience.
18
19 The Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change
20 Adaptation (referred as the SREX report, IPCC, 2012) provided a comprehensive assessment on changes in
21 extremes and how exposure and vulnerability to extremes determine the impacts and likelihood of disasters.
22 Chapter 3 of that report (Seneviratne et al., 2012, hereafter also referred to as SREX Ch3) assessed physical
23 aspects of extremes, and laid a foundation for the follow-up IPCC assessments. Several chapters of the WGI
24 AR5 (IPCC AR5; IPCC, 2013) addressed climate extremes with respect to observed changes (Hartmann et
25 al., 2013), model evaluation (Flato et al., 2013), attribution (Bindoff et al., 2013), and projected long-term
26 changes (Collins et al., 2013). Assessments were also provided in the recent IPCC Special Reports on 1.5°C
27 global warming (SR15, IPCC, 2018; Hoegh-Guldberg et al., 2018), on climate change and land (IPCC,
28 2019), and on oceans and the cryosphere (IPCC, 2019). These assessments are the starting point of the
29 present assessment.
30
31 This chapter is structured as follows (Figure 11.1). This Section (11.1) provides the general framing and
32 introduction to the chapter, highlighting key aspects that underlie the confidence and uncertainty in the
33 assessment of changes in extremes, and introducing some main elements of the chapter. To provide readers a
34 quick overview of past and future changes in extremes, a synthesis of global scale assessment for different
35 types of extremes is included at the end of this Section (Tables 11.1 and 11.2). Section 11.2 introduces
36 methodological aspects of research on climate extremes. Sections 11.3 to 11.7 assess past changes and their
37 attribution to causes, and projected future changes in extremes, for different types of extremes, including
38 temperature extremes, heavy precipitation and pluvial floods, river floods, droughts, and storms, in separate
39 sections. Section 11.8 addresses compound events. Section 11.9 summarizes regional assessments of changes
40 in temperature extremes, in precipitation extremes and in droughts by continents in tables. The chapter also
41 includes several boxes and FAQs on more specific topics.
42
43
44 [START FIGURE 11.1 HERE]
45
46
47 Figure 11.1: Chapter 11 visual abstract of contents.
48
49
50 [END FIGURE 11.1 HERE]
51
52
53 11.1.2 What are extreme events and how are their changes studied?
54
55 Building on the SREX report and AR5, this Report defines an extreme weather event as “an event that is rare
56 at a particular place and time of year” and an extreme climate event as “a pattern of extreme weather that
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1 persists for some time, such as a season” (Annex VII: Glossary). The definitions of rare are wide ranging,
2 depending on applications. Some studies consider an event as an extreme if it is unprecedented; on the other
3 hand, other studies consider events that occur several times a year as moderate extreme events. Rarity of an
4 event with a fixed magnitude also changes under human-induced climate change, making events that are
5 unprecedented so far rather probable under present conditions, but unique in the observational record – and
6 thus often considered as “surprises” (see Box 11.2).
7
8 Various approaches are used to define extremes. These are generally based on the determination of relative
9 (e.g. 90th percentile) or absolute (e.g. 35°C for a hot day) thresholds above which conditions are considered
10 extremes. Changes in extremes can be examined from two perspectives, either focusing on changes in
11 frequency of given extremes, or on changes in their intensity. These considerations in the definition of
12 extremes are further addressed in Section 11.2.1.
13
14
15 11.1.3 Types of extremes assessed in this chapter
16
17 The types of extremes assessed in this chapter include temperature extremes, heavy precipitation and pluvial
18 floods, river floods, droughts, and storms. The drought assessment addresses meteorological droughts,
19 agricultural and ecological droughts, and hydrological droughts (see Annex VII: Glossary). The storms
20 assessment addresses tropical cyclones, extratropical cyclones, and severe convective storms. In addition,
21 this chapter also assesses changes in compound events, that is, multivariate or concurrent extreme events,
22 because of their relevance to impacts as well as the emergence of new literature on the subject. Most of the
23 considered extremes were also assessed in the SREX and AR5. Compound events were not assessed in depth
24 in past IPCC reports (SREX Ch3; Section 11.8). Marine-related extremes such as marine heat waves and
25 extreme sea level, are assessed in Chapter 9 (Section 9.6.4 and Box 9.2) of this report.
26
27 Extremes and related phenomena are of various spatial and temporal scales. Tornadoes have a spatial scale
28 as small as less than 100 meters and a temporal scale as short as a few minutes. In contrast, a drought can last
29 for multiple years, affecting vast regions. The level of complexity of the involved processes differs from one
30 type of extreme to another, affecting our capability to detect, attribute and project changes in weather and
31 climate extremes. Temperature and precipitation extremes studied in the literature are often based on
32 extremes derived from daily values. Studies of events on longer time scales for both temperature or
33 precipitation, or on sub-daily extremes, are scarcer, which generally limits the assessment for such events.
34 Nevertheless, extremes on time scales different from daily are assessed for temperature extremes and heavy
35 precipitation, when possible (Sections 11.3, 11.4). Droughts, as well as tropical and extratropical cyclones,
36 are assessed as phenomena in general, not limited by their extreme forms, because these phenomena are
37 relevant to impacts (Sections 11.6, 11.7). Both precipitation and wind extremes associated with storms are
38 considered.
39
40 Multiple concomitant extremes can lead to stronger impacts than those resulting from the same extremes had
41 they happened in isolation. For this reason, the occurrence of multiple extremes that are multivariate and/or
42 concurrent and/or happening in succession, also called “compound events” (SREX Ch3), are assessed in this
43 chapter based on emerging literature on this topic (Section 11.8). Box 11.2 also provides an assessment on
44 low-likelihood high-impact scenarios associated with extremes.
45
46 The assessment of projected future changes in extremes is presented as function of different global warming
47 levels (Section 11.2.4 and CC-Box 11.1). On the one hand, this provides traceability and comparison to the
48 SR15 assessment (Hoegh-Guldberg et al., 2018, hereafter referred to as SR15 Ch3). On the other hand, this
49 is useful for decision makers as actionable information, as much of the mitigation policy discussion and
50 adaptation planning can be tied to the level of global warming. For example, regional changes in extremes,
51 and thus their impacts, can be linked to global mitigation efforts. Additionally, there is also an advantage of
52 separating uncertainty in future projections due to regional responses as function of global warming levels
53 from other factors such as differences in global climate sensitivity and emission scenarios (CC-Box 11.1).
54 However, information is also provided on the translation between information provided at global warming
55 levels and for single emissions scenarios (CC-Box 11.1) to facilitate easier comparison with the AR5
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1 assessment and with some analyses provided in other chapters as function of emissions scenarios.
2
3 A global-scale synthesis of this chapter’s assessments is provided in Section 11.1.7. In particular, Tables
4 11.1 and 11.2 provide a synthesis for observed and attributed changes, and projected changes in extremes,
5 respectively, at different global warming levels (1.5°C, 2°C, 4°C). Tables on regional-scale assessments for
6 changes in temperature extremes, heavy precipitation and droughts, are provided in Section 11.9.
7
8
9 11.1.4 Effects of greenhouse gas and other external forcings on extremes
10
11 SREX, AR5, and SR15 assessed that there is evidence from observations that some extremes have changed
12 since the mid 20th century, that some of the changes are a result of anthropogenic influences, and that some
13 observed changes are projected to continue into the future, while other changes are projected to emerge from
14 natural climate variability under enhanced global warming (SREX Chapter 3, AR5 Chapter 10).
15
16 At the global scale but also at the regional scale to some extent, many of the changes in extremes are a direct
17 consequence of enhanced radiative forcing, and the associated global warming and/or resultant increase in
18 the water-holding capacity of the atmosphere, as well as changes in vertical stability and meridional
19 temperature gradients that affect climate dynamics (see Box 11.1). Widespread observed and projected
20 increases in the intensity and frequency of hot extremes, together with decreases in the intensity and
21 frequency of cold extremes, are consistent with global and regional warming (Figure 11.2, Section 11.3).
22 Extreme temperatures on land tend to increase more than the global mean temperature (Figure 11.2), due in
23 large part to the land-sea contrast, and additionally to regional feedbacks in some regions (Section 11.1.6).
24 Increases in the intensity of temperature extremes scale robustly and in general linearly with global warming
25 across different geographical regions in projections up to 2100, with minimal dependence on emissions
26 scenarios (Figures 11.3 and 11.A.1; Seneviratne et al., 2016; Wartenburger et al., 2017; Kharin et al., 2018;
27 Section 11.2.4 and CC-Box 11.1). The frequency of hot temperature extremes (see Figure 11.6), the number
28 of heat wave days and the length of heat wave seasons in various regions also scale well, but non-linearly
29 (because of the threshold effect), with global mean temperatures (Wartenburger et al., 2017; Sun et al.,
30 2018a).
31
32 Changes in annual maximum one-day precipitation (Rx1day) are proportional to mean global surface
33 temperature changes, at about 7% increase per 1°C temperature increase, that is, following the Clausius-
34 Clapeyron relationship (Box 11.1), both in observations (Westra et al., 2013) and in future projections
35 (Kharin et al., 2013) at the global scale. Extreme short-duration precipitation in North America also scales
36 with global surface temperature (Li et al., 2018a; Prein et al., 2016b). At the local and regional scales,
37 changes in extremes are also strongly modulated and controlled by regional forcings and feedback
38 mechanisms (Section 11.1.6), whereby some regional forcings, for example, associated with changes in land
39 cover and land or aerosol emissions, can have non-local or some (non-homogeneous) global-scale effects. In
40 general, there is high confidence in changes in extremes due to global-scale thermodynamic processes (i.e.,
41 global warming, mean moistening of the air) as the processes are well understood, while the confidence in
42 those related to dynamic processes or regional and local forcing, including regional and local thermodynamic
43 processes, is much lower due to multiple factors (see following sub-section and Box 11.1).
44
45
46 [START FIGURE 11.2 HERE]
47
48 Figure 11.2: Time series of observed temperature anomalies for global average annual mean temperature (black), land
49 average annual mean temperature (green), land average annual hottest daily maximum temperature (TXx,
50 purple), and land average annual coldest daily minimum temperature (TNn, blue). Global and land mean
51 temperature anomalies are relative to their 1850-1900 means based on the multi-product mean annual
52 time series assessed in Section 2.3.1.1.3 (see text for references). TXx and TNn anomalies are relative to
53 their respective 1961-1990 means and are based on the HadEX3 dataset (Dunn et al., 2020) using values
54 for grid boxes with at least 90% temporal completeness over 1961-2018. Further details on data sources
55 and processing are available in the chapter data table (Table 11.SM.9).
56
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1
2 [END FIGURE 11.2 HERE]
3
4
5 [START FIGURE 11.3 HERE]
6
7
8 Figure 11.3: Regional mean changes in annual hottest daily maximum temperature (TXx) for AR6 land regions and
9 the global land, against changes in global mean surface air temperature (GSAT) as simulated by CMIP6
10 models under different forcing scenarios SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. (a)
11 shows individual models from the CMIP6 ensemble (grey), the multi-model median under three selected
12 SSPs (colours), and the multi-model median (black). (b) to (l) show the multi-model-median for the
13 pooled data for individual AR6 regions. Numbers in parantheses indicate the linear scaling between
14 regional TXx and GSAT. The black line indicates the 1:1 reference scaling between TXx and GSAT. See
15 Atlas.1.3.2 for the definition of regions. For details on the methods see Supplementary Material 11.SM.2.
16
17
18 [END FIGURE 11.3 HERE]
19
20
21 Since AR5, the attribution of extreme weather events, or the investigation of changes in the frequency and/or
22 magnitude of individual and local- and regional-scale extreme weather events due to various drivers (see
23 Cross-Working Group Box 1.1 (in Chapter 1) and Section 11.2.3) has provided evidence that greenhouse
24 gases and other external forcings have affected individual extreme weather events. The events that have been
25 studied are geographically uneven. A few events, for example, extreme rainfall events in the UK (Schaller et
26 al., 2016; Vautard et al., 2016; Otto et al., 2018b) or heat waves in Australia (King et al., 2014; Perkins-
27 Kirkpatrick et al., 2016; Lewis et al., 2017b), have spurred more studies than other events. Many highly
28 impactful extreme weather events have not been studied in the event attribution framework. Studies in the
29 developing world are also generally lacking. This is due to various reasons (Section 11.2) including lack of
30 observational data, lack of reliable climate models, and lack of scientific capacity (Otto et al., 2020). While
31 the events that have been studied are not representative of all extreme events that occurred and results from
32 these studies may also be subject to selection bias, the large number of event attribution studies provide
33 evidence that changes in the properties of these local and individual events are in line with expected
34 consequences of human influence on the climate and can be attributed to external drivers (Section 11.9).
35 Figure 11.4 summarizes assessments of observed changes in temperature extremes, in heavy precipitation
36 and in droughts, and their attribution in a map form.
37
38
39 [START FIGURE 11.4 HERE]
40
41 Figure 11.4: Overview of observed changes for cold, hot, and wet extremes and their potential human
42 contribution. Shown are the direction of change and the confidence in 1) the observed changes in how
43 cold and hot as well as wet extremes have already changed across the world and 2) in the contribution of
44 whether human-induced climate change contributed in causing to these changes (attribution). In each
45 region changes in extremes are indicated by colour (orange – increase in the type of extreme, blue –
46 decrease, both colours – there are changes of opposing direction within the region the signal depends on
47 the exact event definition, grey – there are no changes observed, and no fill – the data/evidence is too
48 sparse to make an assessment). The squares and dots next to the symbol indicate the level of confidence
49 for observing the trend and the human contribution, respectively. The more black dots/squares the higher
50 the level of confidence. The information on this figure is based on regional assessment of the literature on
51 observed trends, detection and attribution and event attribution in section 11.9.
52
53 [END FIGURE 11.4 HERE]
54
55
56
57
58
59
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1 [START BOX 11.1 HERE]
2
3
4 BOX 11.1: Thermodynamic and dynamic changes in extremes across scales
5
6 Changes in weather and climate extremes are determined by local exchanges in heat, moisture, and other
7 related quantities (thermodynamic changes) and those associated with atmospheric and oceanic motions
8 (dynamic changes). While thermodynamic and dynamic processes are interconnected, considering them
9 separately helps to disentangle the roles of different processes contributing to changes in climate extremes
10 (e.g. Shepherd, 2014).
11
12 Temperature extremes
13 An increase in the concentration of greenhouse gases in the atmosphere leads to the warming of tropospheric
14 air and the Earth’s surface. This direct thermodynamic effect leads to warmer temperatures everywhere with
15 an increase in the frequency and intensity of warm extremes and a decrease in the frequency and intensity of
16 cold extremes. The initial increase in temperature in turn leads to other thermodynamic responses and
17 feedbacks affecting both the atmosphere and the surface. These include an increase in the water vapour
18 content of the atmosphere (water vapour feedback, see Section 7.4.2.2) and a change in the vertical profile of
19 temperature (e.g., lapse rate feedback, see Section 7.4.2.2). While the water vapour feedback always
20 amplifies the initial temperature increases (positive feedback), the lapse rate feedback amplifies near-surface
21 temperature increases (positive feedback) in mid- and high latitudes but reduces temperature increases
22 (negative feedback) in tropical regions (Pithan and Mauritsen, 2014).
23
24 Thermodynamic responses and feedbacks also occur through surface processes. For instance, observations
25 and model simulations show that temperature increases, including extreme temperatures, are amplified in
26 areas where seasonal snow cover is reduced due to decreases in surface albedo (see Section 11.3.1). In some
27 mid-latitude areas, temperature increases are amplified by the higher atmospheric evaporative demand (Fu
28 and Feng, 2014; Vicente-Serrano et al., 2020b) that results in a drying of soils in some regions (Section
29 11.6), leading to increased sensible heat fluxes (soil-moisture temperature feedback, see Sections 11.1.6 and
30 11.3.1). Other thermodynamic feedback processes include changes in the water-use efficiency of plants
31 under enhanced atmospheric CO2 concentrations that can reduce the overall transpiration, and thus also
32 enhance temperature in projections (Sections 8.2.3.3, 11.1.6, 11.3, and 11.6).
33
34 Changes in the spatial distribution of temperatures can also affect temperature extremes by modifying the
35 characteristics of weather patterns (e.g., Suarez-Gutierrez et al., 2020). For example, a robust thermodynamic
36 effect of polar amplification is a weakened north-south temperature gradient, which amplifies the warming
37 of cold extremes in the Northern Hemisphere mid- and high latitudes because of the reduction of cold air
38 advection (Holmes et al., 2015; Schneider et al., 2015; Gross et al., 2020). Much less robust is the dynamic
39 effect of polar amplification (Section 7.4.4.1) and the reduced low-altitude meridional temperature gradient
40 that has been linked to an increase in the persistence of weather patterns (e.g., heatwaves) and subsequent
41 increases in temperature extremes (Francis and Vavrus, 2012; Coumou et al., 2015, 2018; Mann et al., 2017)
42 (CC-Box 10.1).
43
44 Precipitation extremes
45 Changes in temperature also control changes in water vapour through increases in evaporation and in the
46 water-holding capacity of the atmosphere (Section 8.2.1). At the global scale, column-integrated water
47 vapour content increases roughly following the Clausius-Clapeyron (C-C) relation, with an increase of
48 approximately 7% for every degree celsius of global-mean surface warming (Section 8.2.1). Nonetheless, at
49 regional scales, water vapour increases differ from this C-C rate due to several reasons (Section 8.2.2),
50 including a change in weather regimes and limitations in moisture transport from the ocean, which warms
51 more slowly than land (Byrne and O’Gorman, 2018). Observational studies (Fischer and Knutti, 2016; Sun et
52 al., 2020) have shown the observed rate of increase of precipitation extremes is similar to the C-C scaling at
53 the global scale. Climate model projections show that the increase in water vapour leads to robust increases
54 in precipitation extremes everywhere, with a magnitude that varies between 4% and 8% per degree celsius of
55 surface warming (thermodynamic contribution, Box 11.1, Figure 1b). At regional scales, climate models
56 show that the dynamic contribution (Box 11.1, Figure 1c) can be substantial and strongly modify the
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1 projected rate of change of extreme precipitation (Box 11.1, Figure 1a) with large regions in the subtropics
2 showing robust reductions and other areas (e.g., equatorial Pacific) showing robust amplifications (Box 11.1,
3 Figure 1c). However, the dynamic contributions show large differences across models and are more
4 uncertain than thermodynamic contributions (Shepherd, 2014; Trenberth et al., 2015; Pfahl et al., 2017; Box
5 11.1, Figure 1c).
6
7 Dynamic contributions can occur in response to changes in the vertical and horizontal distribution of
8 temperature (thermodynamics) and can affect the frequency and intensity of synoptic and subsynoptic
9 phenomena including tropical cyclones, extratropical cyclones, fronts, mesoscale-convective systems and
10 thunderstorms. For example, the poleward shift and strengthening of the Southern Hemisphere mid-latitude
11 storm tracks (Section 4.5.1) can modify the frequency/intensity of extreme precipitation. However, the
12 precise way in which dynamic changes will affect precipitation extremes is unclear due to several competing
13 effects (Shaw et al., 2016; Allan et al., 2020).
14
15 Extreme precipitation can also be enhanced by dynamic responses and feedbacks occurring within storms
16 that result from the extra latent heat released from the thermodynamic increases in moisture (Lackmann,
17 2013; Willison et al., 2013; Marciano et al., 2015; Nie et al., 2018; Mizuta and Endo, 2020). The extra latent
18 heat released within storms has been shown to increase precipitation extremes by strengthening convective
19 updrafts and the intensity of the cyclonic circulation (e.g., Molnar et al., 2015; Nie et al., 2018), although
20 weakening effects have also been found in mid-latitude cyclones (e.g., Kirshbaum et al., 2017). Additionally,
21 the increase in latent heat can also suppress convection at larger scales due to atmospheric stabilization (Nie
22 et al., 2018; Tandon et al., 2018; Kendon et al., 2019). As these dynamic effects result from feedback
23 processes within storms where convective processes are crucial, their proper representation might require
24 improving the horizontal/vertical resolution, the formulation of parameterizations, or both, in current climate
25 models (i.e., Ban et al., 2015; Kendon et al., 2014; Meredith et al., 2015; Nie et al., 2018; Prein et al., 2015;
26 Westra et al., 2014).
27
28
29 [START BOX 11.1, FIGURE 1 HERE]
30
31 Box 11.1, Figure 1: Multi-model (CMIP5) mean fractional changes (in % per degree of warming) for (a) annual
32 maximum precipitation (Rx1day), (b) changes in Rx1day due to the thermodynamic contribution
33 and (c) changes in Rx1day due to the dynamic contribution estimated as the difference between
34 the total changes and the thermodynamic contribution. Changes were derived from a linear
35 regression for the period 1950–2100. Uncertainty is represented using the simple approach: no
36 overlay indicates regions with high model agreement, where ≥80% of models (n=22) agree on sign
37 of change; diagonal lines indicate regions with low model agreement, where <80% of models
38 agree on sign of change. For more information on the simple approach, please refer to the Cross-
39 Chapter Box Atlas 1. A detailed description of the estimation of dynamic and thermodynamic
40 contributions is given in Pfahl et al. (2017). Adapted from (Pfahl et al., 2017), originally published
41 in Nature Climate Change/ Springer Nature. Further details on data sources and processing are
42 available in the chapter data table (Table 11.SM.9).
43
44 [END BOX 11.1, FIGURE 1 HERE]
45
46
47 Droughts
48 Droughts are also affected by both thermodynamic and dynamic processes (Sections 8.2.3.3 and 11.6).
49 Thermodynamic processes affect droughts by increasing atmospheric evaporative demand (Martin, 2018;
50 Gebremeskel Haile et al., 2020; Vicente-Serrano et al., 2020b) through changes in air temperature, radiation,
51 wind speed, and relative humidity. Dynamic processes affect droughts through changes in the occurrence,
52 duration and intensity of weather anomalies, which are related to precipitation and the amount of sunlight
53 (Section 11.6). While atmospheric evaporative demand increases with warming, regional changes in aridity
54 are affected by increasing land-ocean warming contrast, vegetation feedbacks and responses to rising CO2
55 concentrations and dynamic shifts in the location of the wet and dry parts of the atmospheric circulation in
56 response to climate change as well as internal variability (Byrne and O’Gorman, 2015; Kumar et al., 2015;
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1 Allan et al., 2020).
2
3 In summary, both thermodynamic and dynamic processes are involved in the changes of extremes in
4 response to warming. Anthropogenic forcing (e.g., increases in greenhouse gas concentrations) directly
5 affects thermodynamic variables, including overall increases in high temperatures and atmospheric
6 evaporative demand, and regional changes in atmospheric moisture, which intensify heatwaves, droughts and
7 heavy precipitation events when they occur (high confidence). Dynamic processes are often indirect
8 responses to thermodynamic changes, are strongly affected by internal climate variability and are also less
9 well understood. As such, there is low confidence in how dynamic changes affect the location and magnitude
10 of extreme events in a warming climate.
11
12 [END BOX 11.1 HERE]
13
14
15 11.1.5 Effects of large-scale circulation on changes in extremes
16
17 Atmospheric large-scale circulation patterns and associated atmospheric dynamics are important
18 determinants of the regional climate (Chapter 10). As a result, they are also important to the magnitude,
19 frequency, and duration of extremes (Box 11.4). Aspects of changes in large-scale circulation patterns are
20 assessed in Chapters 2, 3, 4, and 8 and representative atmospheric and oceanic modes are described in Annex
21 IV. This subsection provides some general concepts, through a couple of examples, on why the uncertainty
22 in the response of large-scale circulation patterns to external forcing can cascade to uncertainty in the
23 response of extremes to external forcings. Details for specific types of extremes are covered in the relevant
24 subsections. For example, the occurrence of the El Niño-Southern Oscillation (ENSO) influences
25 precipitation regimes in many areas, favoring droughts in some regions and heavy rains in others (Box 11.4).
26 The extent and strength of the Hadley circulation influences regions where tropical and extra-tropical
27 cyclones occur, with important consequences for the characteristics of extreme precipitation, drought, and
28 winds (Section 11.7). Changes in circulation patterns associated with land-ocean heat contrast, which affect
29 the monsoon circulations (Section 8.4.2.4), lead to heavy precipitation along the coastal regions in East Asia
30 (Freychet et al., 2015). As a result, changes in the spatial and/or temporal variability of the atmospheric
31 circulation in response to warming affect characteristics of weather systems such as tropical cyclones
32 (Sharmila and Walsh, 2018), storm tracks (Shaw et al., 2016), and atmospheric rivers (Waliser and Guan,
33 2017) (e.g. Section 11.7). Changes in weather systems come with changes in the frequency and intensity of
34 extreme winds, extreme temperatures, and extreme precipitation, on the backdrop of thermodynamic
35 responses of extremes to warming (Box 11.1). Floods are also affected by large-scale circulation modes,
36 including ENSO, the North Atlantic Oscillation (NAO), the Atlantic Multi-decadal Variability (AMV), and
37 the Pacific Decadal Variability (PDV) (Kundzewicz et al., 2018; Annex IV). Aerosol forcing, through
38 changes in patterns of sea surface temperatures (SSTs), also affects circulation patterns and tropical cyclone
39 activities (Takahashi et al., 2017).
40
41 Changes in atmospheric large-scale circulation due to external forcing are uncertain in general, but there are
42 clear signals in some aspects (Chapter 2, 3, 4, and 8; Sections 2.3.1.4, 8.2.2.2). Among them, there has been
43 a very likely widening of the Hadley circulation since the 1980s and the extratropical jets and cyclone tracks
44 have likely been shifting poleward since the 1980s (Section 2.3.1.4). The poleward expansion affects drought
45 occurrence in some regions (Section 11.6), and results in poleward shifts of tropical cyclones and storm
46 tracks (Sections 11.7.1, 11.7.2). Although it is very likely that the amplitude of ENSO variability will not
47 robustly change over the 21st century (Section 4.3.3.2), the frequency of extreme El Niños (Box 11.4),
48 defined by precipitation threshold, is projected to increase with global warming (Section 6.5 of SROCC).
49 This would have implications for projected changes in extreme events affected by ENSO, including droughts
50 over wide areas (Section 11.6; Box 11.4) and tropical cyclones (Section 11.7.1). A case study is provided for
51 extreme ENSOs in 2015/2016 in Box 11.4 to highlight the influence of ENSO on extremes.
52
53 In summary, large-scale atmospheric circulation patterns are important drivers for local and regional
54 extremes. There is overall low confidence about future changes in the magnitude, frequency, and spatial
55 distribution of these patterns, which contributes to uncertainty in projected responses of extremes, especially
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1 in the near term.
2
3
4 11.1.6 Effects of regional-scale processes and forcings and feedbacks on changes in extremes
5
6 At the local and regional scales, changes in extremes are strongly modulated by regional and local feedbacks
7 (SRCCL, Jia et al., 2019; Seneviratne et al., 2013; Miralles et al., 2014; Lorenz et al., 2016; Vogel et al.,
8 2017), changes in large-scale circulation patterns (11.1.5), and regional forcings such as changes in land use
9 or aerosol concentrations (Chapters 3 and 7; Hirsch et al., 2017, 2018; Thiery et al., 2017; Wang et al.,
10 2017f; Findell et al., 2017). In some cases, such responses may also include non-local effects (e.g., Persad
11 and Caldeira, 2018; Miralles et al., 2019; de Vrese et al., 2016; Schumacher et al., 2019). Regional-scale
12 forcing and feedbacks often affect temperature distributions asymmetrically, with generally higher effects for
13 the hottest percentiles (Section 11.3).
14
15 Land use can affect regional extremes, in particular hot extremes, in several ways (high confidence). This
16 includes effects of land management (e.g. cropland intensification, irrigation, double cropping) and well as
17 of land cover changes (deforestation) (Section 11.3.2; see also 11.6). Some of these processes are not well
18 represented (e.g. effects of forest cover on diurnal temperature cycle) or not integrated (e.g. irrigation) in
19 climate models (Sections 11.3.2, 11.3.3). Overall, the effects of land use forcing may be particularly relevant
20 in the context of low-emissions scenarios, which include large land use modifications, for instance associated
21 with the expansion of biofuels, biofuels with carbon capture and storage (BECCS), or re-afforestation to
22 ensure negative emissions, as well as with the expansion of food production (e.g. SR15, Chapter 3; CC-Box
23 5.1; van Vuuren et al., 2011, Hirsch et al., 2018). There are also effects on the water cycle through
24 freshwater use (CC-Box 5.1; Section 11.6).
25
26 Aerosol forcing also has a strong regional footprint associated with regional emissions, which affects
27 temperature and precipitation extremes (high confidence; Sections 11.3, 11.4). From ca. the 1950s to 1980s,
28 enhanced aerosol loadings led to regional cooling due to decreased global solar radiation (“global dimming”)
29 which was followed by a phase of “global brightening” due to a reduction in aerosol loadings (Chapters 3
30 and 7; Wild et al., 2005). King et al. (2016a) show that aerosol-induced cooling delayed the timing of a
31 significant human contribution to record-breaking heat extremes in some regions. On the other hand, the
32 decreased aerosol loading since the 1990s has led to an accelerated warming of hot extremes in some
33 regions. Based on Earth System Model (ESM) simulations, Dong et al. (2017b) suggest that a substantial
34 fraction of the warming of the annual hottest days in Western Europe since the mid-1990s has been due to
35 decreases in aerosol concentrations in the region. Dong et al. (2016) also identify non-local effects of
36 decreases in aerosol concentrations in Western Europe, which they estimate played a dominant role in the
37 warming of the hottest daytime temperatures in Northeast Asia since the mid-1990s, via induced coupled
38 atmosphere-land surface and cloud feedbacks, rather than a direct impact of anthropogenic aerosol changes
39 on cloud condensation nuclei.
40
41 In addition to regional forcings, regional feedback mechanisms can also substantially affect extremes (high
42 confidence; Sections 11.3, 11.4, 11.6). In particular, soil moisture feedbacks play an important role for
43 extremes in several mid-latitude regions, leading in particular to a marked additional warming of hot
44 extremes compared to mean global warming (Seneviratne et al., 2016; Bathiany et al., 2018; Miralles et al.,
45 2019), which is superimposed on the known land-sea contrast in mean warming (Vogel et al., 2017). Soil
46 moisture-atmosphere feedbacks also affect drought development (Section 11.6). Additionally, effects of land
47 surface conditions on circulation patterns have also been reported (Koster et al., 2016; Sato and Nakamura,
48 2019). These regional feedbacks are also associated with substantial spread in models (Section 11.3), and
49 contribute to the identified higher spread of regional projections of temperature extremes as function of
50 global warming, compared with the spread resulting from the differences in projected global warming
51 (global transient climate responses) in climate models (Seneviratne and Hauser, 2020). In addition, there are
52 also feedbacks between soil moisture content and precipitation occurrence, generally characterized by
53 negative spatial feedbacks and positive local feedbacks (Taylor et al., 2012; Guillod et al., 2015). Climate
54 model projections suggest that these feedbacks are relevant for projected changes in heavy precipitation
55 (Seneviratne et al., 2013), however, there is evidence that climate models do not capture the correct sign of
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1 the soil moisture-precipitation feedbacks in several regions, in particular spatially and/or in some cases also
2 temporally (Taylor et al., 2012; Moon et al., 2019). In the Northern Hemisphere high latitudes, the snow- and
3 ice-albedo feedback, along with other factors, is projected to largely amplify temperature increases (e.g.,
4 Pithan and Mauritsen, 2014), although the effect on temperature extremes is still unclear. It is also still
5 unclear whether snow-albedo feedbacks in mountainous regions might have an effect on temperature and
6 precipitation extremes (e.g. Gobiet et al., 2014), however these feedbacks play an important role in projected
7 changes in high-latitude warming (Hall and Qu, 2006), and, in particular, in changes in cold extremes in
8 these regions (Section 11.3).
9
10 Finally, extreme events may also regionally amplify one another. This is, e.g., the case for heat waves and
11 droughts, with high temperatures and stronger radiative forcing leading to drying tendencies on land due to
12 increased evapotranspiration (Section 11.6), and drier soils then inducing decreased evapotranspiration and
13 higher sensible heat flux and hot temperatures (Seneviratne et al., 2013; Miralles et al., 2014; Vogel et al.,
14 2017; Zscheischler and Seneviratne, 2017; Zhou et al., 2019b; Kong et al., 2020; see Box 11.1, Section
15 11.8).
16
17 In summary, regional forcings and feedbacks, in particular associated with land use and aerosol forcings, and
18 soil moisture-temperature, soil moisture-precipitation, and snow/ice-albedo-temperature feedbacks, play an
19 important role in modulating regional changes in extremes. These can also lead to a higher warming of
20 extreme temperatures compared to mean temperature (high confidence), and possibly cooling in some
21 regions (medium confidence). However, there is only medium confidence in the representation of the
22 associated processes in state-of-the-art Earth System Models.
23
24
25 11.1.7 Global-scale synthesis
26
27 Tables 11.1 and 11.2 provide a synthesis for observed and attributed changes in extremes, and projected
28 changes in extremes, respectively, at different levels of global warming. This synthesis assessment focuses
29 on the more likely range of observed and projected changes. However, some low-likelihood high-impact
30 scenarios can also be of high relevance as addressed in Box 11.2.
31
32 Figure 11.5 provides a synthesis on the level of confidence in the attribution and projection of changes in
33 extremes, building on the assessments from Tables 11.1 and 11.2. In the case where the physical processes
34 underlying the changes in extremes in response to human forcing are well understood and the signal in the
35 observations is still relatively weak, confidence in the projections would be higher than in the attribution
36 because of an increase in the signal to noise ratio with higher global warming. On the other hand, when the
37 observed signal is already strong and when observational evidence is consistent with model simulated
38 responses, confidence in attribution may be higher than that in projections if certain physical processes could
39 be expected to behave differently in a much warmer world and under much higher greenhouse gas forcing,
40 and if such a behavior is poorly understood.
41
42 Further synthesis figures for regional assessments are provided in Figure 11.4 (event attribution), Figure 11.6
43 (projected change in hot temperature extremes) and Figure 11.7 (projected changes in precipitation
44 extremes), and a synthesis on regional assessments for observed, attributed and projected changes in
45 extremes is provided in Section 11.9 for all AR6 reference regions (See Chapter 1, section 1.4.5 and Figure
46 1.18 for definition of AR6 regions).
47
48 Confidence and likelihood of past changes and projected future changes at 2°C of global warming lon the
49 global scale. The information in this figure is based on Tables 11.1 and 11.2.
50 [START FIGURE 11.5 HERE]
51
52
53 Figure 11.5: Confidence and likelihood of past changes and projected future changes at 2°C of global warming on the
54 global scale. The information in this figure is based on Tables 11.1 and 11.2.
55
56
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1 [END FIGURE 11.5 HERE]
2
3
4 [START FIGURE 11.6 HERE]
5
6
7 Figure 11.6: Projected changes in the frequency of extreme temperature events under 1°C, 1.5°C, 2°C, 3°C, and 4°C
8 global warming levels relative to the 1851-1900 baseline. Extreme temperatures are defined as the
9 maximum daily temperatures that were exceeded on average once during a 10-year period (10-year event,
10 blue) and once during a 50-year period (50-year event, orange) during the 1851-1900 base period. Results
11 are shown for the global land and the AR6 regions. For each box plot, the horizontal line and the box
12 represent the median and central 66% uncertainty range, respectively, of the frequency changes across the
13 multi model ensemble, and the whiskers extend to the 90% uncertainty range. The dotted line indicates no
14 change in frequency. The results are based on the multi-model ensemble from simulations of global
15 climate models contributing to the sixth phase of the Coupled Model Intercomparison Project (CMIP6)
16 under different SSP forcing scenarios. Adapted from (Li et al., 2020a). Further details on data sources and
17 processing are available in the chapter data table (Table 11.SM.9).
18
19
20 [END FIGURE 11.6 HERE]
21
22
23 [START FIGURE 11.7 HERE]
24
25
26 Figure 11.7: Projected changes in the frequency of extreme precipitation events under 1°C, 1.5°C, 2°C, 3°C, and 4°C
27 global warming levels relative to the 1951-1990 baseline. Extreme precipitation is defined as the
28 maximum daily precipitation (Rx1day) that was exceeded on average once during a 10-year period (10-
29 year event, blue) and once during a 50-year period (50-year event, orange) during the 1851-1900 base
30 period. Results are shown for the global land and the AR6 regions. For each box plot, the horizontal line
31 and the box represent the median and central 66% uncertainty range, respectively, of the frequency
32 changes across the multi model ensemble, and the whiskers extend to the 90% uncertainty range. The
33 dotted line indicates no change in frequency. The results are based on the multi-model ensemble from
34 simulations of global climate models contributing to the sixth phase of the Coupled Model
35 Intercomparison Project (CMIP6) under different SSP forcing scenarios. Adapted from (Li et al., 2020a).
36 Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).
37
38
39 [END FIGURE 11.7 HERE]
40
41
42 [START TABLE 11.1 HERE]
43
44
45 Table 11.1: Synthesis table on observed changes in extremes and contribution by human influences. Note that
46 observed changes in marine extremes are assessed in the Cross-Chapter Box 9.1 in Chapter 9.
47
Phenomenon and direction of Observed/detected trends since 1950 (for Human contribution to the observed trends since
trend +0.5°C global warming or higher) 1950 (for +0.5°C global warming or higher)
Warmer and/or more frequent Virtually certain on global scale {11.3} Extremely likely main contributor on global scale
hot days and nights over most {11.3}
land areas Continental-scale evidence:
Warmer and/or fewer cold Asia, Australasia, Europe, North America: Continental-scale evidence:
Very likely North America, Europe, Australasia, Asia: Very
days and nights over most
land areas Central and South America: High likely
confidence Central and South America: High confidence
Warm spells/heat waves; Africa: Medium confidence Africa: Medium confidence
Increases in frequency or {11.3, 11.9}
intensity over most land areas {11.3, 11.9}
Cold spells/cold waves:
Decreases in frequency or
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intensity over most land areas
Heavy precipitation events: Likely on global scale, over majority of Likely main contributor to the observed
increase in the frequency, land regions with good observational intensification of heavy precipitation in land
intensity, and/or amount of coverage {11.3} regions on global scale.
heavy precipitation {11.3}
Continental-scale evidence:
Continental-scale evidence:
Asia, Europe, North America: Likely
Asia, Europe, North America: Likely
Africa, Australasia, Central and South
America: Low confidence Africa, Australasia, Central and South America:
Low confidence
{11.3, 11.9}
{11.3, 11.9}
Agricultural and ecological Medium confidence, in predominant Medium confidence, in predominant fraction of
drought events: Enhanced fraction of land area land area
drying in dry season Observed decrease in water availability in Human contribution to decrease in water
the dry season due to increased availability in the dry season in a predominant
evapotranspiration (driven by increased fraction of the land area (medium confidence)
atmospheric evaporative demand) in a {11.6}
predominant fraction of the land area
(medium confidence) {11.6}
Increasing trends in agricultural and
ecological droughts have been observed in
AR6 regions on all continents (medium
confidence) {11.6, 11.9}
Increase in precipitation Medium confidence High confidence
associated with tropical {11.7} {11.7}
cyclones
Increase in likelihood that a Likely Medium confidence
TC will be at major TC {11.7} {11.7}
intensity (Cat. 3-5)
Changes in frequency of Likely Medium confidence
rapidly intensifying tropical {11.7} {11.7}
cyclones
Poleward migration of Medium confidence Medium confidence
tropical cyclones in the {11.7} {11.7}
western Pacific
Decrease in TC forward It is likely that TC translation speed has It is more likely than not that the slowdown of TC
motion over the USA slowed over the USA since 1900. translation speed over the USA has contributions
{11.7} from anthropogenic forcing.
{11.7}
Severe convective storms Low confidence in past trends in hail and Low confidence.
(tornadoes, hail, rainfall, winds and tornado activity due to short {11.7}
wind, lightning) length of high-quality data records. {11.7}
Increase in compound events Likely increase in the probability of Likely that human-induced climate change has
compound events. increased the probability of compound events.
High confidence that co-occurrent heat High confidence that human influence has
waves and droughts are becoming more increased the frequency of co-occurrent heat
frequent under enhanced greenhouse gas waves and droughts.
forcing at global scale. Medium confidence that human influence has
Medium confidence that fire weather, i.e. increased fire weather occurrence in some regions.
compound hot, dry and windy events, have Low confidence that human influences has
become more frequent in some regions. contributed to changes in compound events
Medium confidence that compound leading to flooding.
flooding risk has increased along the USA {11.8}
coastline.
{11.8}
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1
2 [END TABLE 11.1 HERE]
3
4
5 [START TABLE 11.2 HERE]
6
7 Table 11.2: Synthesis table on projected changes in extremes. Note that projected changes in marine extremes are
8 assessed in Chapter 9 and the Cross-chapter box 9.1 (marine heat waves). Assessments are provided
9 compared to pre-industrial conditions.
10
Phenomenon and Projected changes at +1.5°C Projected changes at +2°C global Projected changes at +4°C global
direction of trend global warming warming warming
Warmer and/or Virtually certain compared to Virtually certain compared to Virtually certain compared to pre-
more frequent hot pre-industrial on global scale. pre-industrial on global scale. industrial on global scale.
days and nights
over most land Extremely likely on all continents Virtually certain on all Virtually certain on all continents
areas continents
Highest increase of temperature Highest increase of temperature of
Warmer and/or of hottest days is projected in Highest increase of temperature hottest days is projected in some
fewer cold days some mid-latitude and semi-arid of hottest days is projected in mid-latitude and semi-arid
and nights over regions, at about 1.5 times to some mid-latitude and semi-arid regions, at about 1.5 times to
most land areas twice the rate of global warming regions, at about 1.5 times to twice the rate of global warming
(high confidence) twice the rate of global warming (high confidence)
Warm spells/heat (high confidence)
waves; Increases in Highest increase of temperature Highest increase of temperature of
frequency or of coldest days is projected in Highest increase of temperature coldest days is projected in Arctic
intensity over most Arctic regions, at about three of coldest days is projected in regions, at about three times the
land areas times the rate of global warming Arctic regions, at about three rate of global warming (high
(high confidence) times the rate of global warming confidence)
Cold spells/cold {11.3} (high confidence) {11.3}
waves: Decreases {11.3}
in frequency or Continental-scale projections: Continental-scale projections:
intensity over most Extremely likely: Africa, Asia, Continental-scale projections: Virtually certain: Africa, Asia,
land areas Australasia, Central and South Virtually certain: Africa, Asia, Australasia, Central and South
America, Europe, North America Australasia, Central and South America, Europe, North America
{11.3, 11.9}} America, Europe, North America {11.3, 11.9}
{11.3, 11.9}
Heavy High confidence that increases Likely that increases take place in Very likely that increases take
precipitation take place in most land regions most land regions place in most land regions
events: increase in {11.4} {11.4} {11.4}
the frequency,
intensity, and/or Very likely: Asia, N. America Extremely likely: Asia, N. Virtually certain: Africa, Asia, N.
amount of heavy Likely: Africa, Europe America America
precipitation High confidence: Central and Very likely: Africa, Europe Extremely likely: Central and
South America Likely: Australasia, Central and South America, Europe
Medium confidence: Australasia South America Very likely Australasia
{11.4, 11.9} {11.4, 11.9} {11.4, 11.9}
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Agricultural and High confidence over Likely over predominant fraction Very likely over predominant
ecological predominant fraction of land area of land area fraction of land area
droughts: Increases
in intensity and/or Land area affected by increasing Land area affected by increasing Land area affected by increasing
duration of drought drought frequency and severity drought frequency and severity drought frequency and severity
events expands with increasing global expands with increasing global expands with increasing global
warming (high confidence). warming (likely). {11.6, 11.9} warming (very likely). {11.6,
{11.6, 11.9} 11.9}
Precipitation decreases is going
Precipitation decreases is going to increase the severity of Precipitation decreases is going to
to increase the severity of drought in some regions; increase the severity of drought in
drought in some regions; atmospheric evaporative demand several regions; atmospheric
atmospheric evaporative demand will continue to increase evaporative demand will continue
will continue to increase compared to pre-industrial to increase compared to pre-
compared to pre-industrial conditions and lead to further industrial conditions and lead to
conditions and lead to further increases in agricultural and further increases in agricultural
increases in agricultural and ecological droughts due to and ecological droughts due to
ecological droughts due to increased evapotranspiration in increased evapotranspiration in
increased evapotranspiration in some regions. (high confidence) several regions. (high confidence)
some regions. (high confidence) {11.6, 11.9} {11.6, 11.9}
{11.6, 11.9}
Increase in High confidence in a projected High confidence in a projected High confidence in a projected
precipitation increase of TC rain rates at the increase of TC rain rates at the increase of TC rain rates at the
associated with global scale; the median global scale; the median global scale; the median projected
tropical cyclones projected rate of increase due to projected rate of increase due to rate of increase due to human
(TC) human emissions is about 11%. human emissions is about 14%. emissions is about 28%. {11.7}
{11.7} {11.7}
Medium confidence that rain rates
Medium confidence that rain Medium confidence that rain will increase in every basin.
rates will increase in every basin. rates will increase in every basin. {11.7}
{11.7} {11.7}
Increase in mean Medium confidence High confidence High confidence
tropical cyclone {11.7} {11.7} {11.7}
lifetime-maximum
wind speed
(intensity)
Increase in High confidence for an increase High confidence for an increase High confidence for an increase in
likelihood that a in the proportion of TCs that in the proportion of TCs that the proportion of TCs that reach
TC will be at major reach the strongest (Category 4- reach the strongest (Category 4- the strongest (Category 4-5)
TC intensity (Cat. 5) levels. The median projected 5) levels. The median projected levels. The median projected
4-5) increase in this proportion is increase in this proportion is increase in this proportion is about
about 10%. about 13%. 20%.
{11.7} {11.7} {11.7}
Severe convective There is medium confidence that the frequency of severe convective storms increases in the spring with
storms enhancement of convective available potential energy (CAPE), leading to extension of seasons of
occurrence of severe convective storms. There is high confidence of future intensification of precipitation
associated with severe convective storms. {11.7}
Increase in Likely that probability of compound events will continue to increase with global warming.
compound events High confidence that co-occurrent heat waves and droughts will continue to increase under higher levels of
(frequency, global warming, with higher frequency/intensity with every additional 0.5°C of global warming.
intensity)
High confidence that fire weather, i.e. compound hot, dry and windy events, will become more frequent in
some regions at higher levels of global warming.
Medium confidence that compound flooding at the coastal zone will increase under higher levels of global
warming, with higher frequency/intensity with every additional 0.5°C of global warming.
{11.8}
1
23
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1 [END TABLE 11.2 HERE]
2
3
4 [START BOX 11.2 HERE]
5
6
7 BOX 11.2: Low-likelihood high-impact changes in extremes
8
9 SREX (Chapter 3) assigned low confidence to low-probability high-impact (LLHI) events. Such events are
10 often not anticipated and thus sometimes referred to as surprises. There are several types of LLHI events.
11 Abrupt changes in mean climate are addressed in Chapter 4. Unanticipated LLHI events can either result
12 from tipping points in the climate system (Section 1.4.4.3), such as the shutdown of the Atlantic
13 thermohaline circulation (SROCC Ch6; Collins et al., 2019) or the drydown of the Amazonian rainforest
14 (SR15 Ch3; Hoegh-Guldberg et al., 2018; Drijfhout et al. 2015), or from uncertainties in climate processes
15 including climate feedbacks that may enhance or damp extremes either related to global or regional climate
16 responses (Seneviratne et al., 2018b; Sutton, 2018). The low confidence does not by itself exclude the
17 possibility of such events to occur, it is instead an indication of a poor state of knowledge. Such outcomes,
18 while unlikely, could be associated with very high impacts, and are thus highly relevant from a risk
19 perspective (see Chapter 1, Section 1.4.3, Box 11.4; Sutton, 2018, 2019). Alternatively, high impacts can
20 occur when different extremes occur at the same time or in short succession at the same location or in several
21 regions with shared vulnerability (e.g. food-basket regions Gaupp et al., 2019). These “compound events”
22 are assessed in Section 11.8 and Box 11.4 provides a case-study example.
23
24 The difficulties in determining the likelihood of occurrence and time frame of potential tipping points and
25 LLHI events persist. However, new literature has emerged on unanticipated and low-probability high-impact
26 events more generally. There are events that are sufficiently rare that they have not been observed in
27 meteorological records, but whose occurrence is nonetheless plausible within the current state of the climate
28 system, see examples below and McCollum et al. (2020). The rare nature of such events and the limited
29 availability of relevant data makes it difficult to estimate their occurrence probability and thus gives little
30 evidence on whether to include such hypothetical events in planning decisions and risk assessments. The
31 estimation of such potential surprises is often limited to events that have historical analogues (including
32 before the instrumental records began, Wetter et al., 2014), albeit the magnitude of the event may differ.
33 Additionally, there is also a limitation of available resources to exhaust all plausible trajectories of the
34 climate system. As a result, there will still be events that cannot be anticipated. These events can be surprises
35 to many in that the events have not been experienced, although their occurrence could be inferred by
36 statistical means or physical modelling approaches (Chen et al., 2017; van Oldenborgh et al., 2017;
37 Harrington and Otto, 2018a). Another approach focusing on the estimation of low-probability events and of
38 events whose likelihood of occurrence is unknown consists in using physical climate models to create a
39 physically self-consistent storyline of plausible extreme events and assessing their impacts and driving
40 factors in past (Section 11.2.3) or future conditions (11.2.4) (Cheng et al., 2018; Schaller et al., 2020;
41 Shepherd, 2016; Shepherd et al., 2018; Sutton, 2018; Zappa and Shepherd, 2017; Wehrli et al., 2020;
42 Hazeleger et al., 2015).
43
44 In many parts of the world, observational data are limited to 50-60 years. This means that the chance to
45 observe an extreme event that occurs once in several hundred or more years is small. Thus, when a very
46 extreme event occurs, it becomes a surprise to many (Bao et al., 2017; McCollum et al., 2020), and very rare
47 events are often associated with high impacts (van Oldenborgh et al., 2017; Philip et al., 2018b; Tozer et al.,
48 2020). Attributing and projecting very rare events in a particular location by assessing their likelihood of
49 occurrence within the same larger region and climate thus provides another way to make quantitative
50 assessments regarding events that are extremely rare locally. Some examples of such events include for
51 instance:
52 • Hurricane Harvey, that made landfall in Houston, TX in August 2017 (Section 11.7.1.4.)
53 • The 2010-2011 extreme floods in Queensland, Australia (Christidis et al., 2013a)
54 • The 2018 concurrent heat waves across the northern Hemisphere (Box 11.4)
55 • Tropical cyclone Idai in Mozambique (Cross-Chapter Box Disaster in WGII AR6 Chapter 4)
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1 • The California fires in 2018 and 2019
2 • The 2019-2020 Australia fires (Cross-Chapter Box Disaster in WGII AR6 Chapter 4)
3
4 One factor making such events hard to anticipate is the fact that we now live in a non-stationary climate, and
5 that the framework of reference for adaptation is continuously moving. As an example, the concurrent heat
6 waves that occurred across the Northern Hemisphere in the summer of 2018 were considered very unusual
7 and were indeed unprecedented given the total area that was concurrently affected (Toreti et al., 2019; Vogel
8 et al., 2019; Drouard et al., 2019; Kornhuber et al., 2019); however, the probability of this event under 1°C
9 global warming was found to be about 16% (Vogel et al., 2019), which is not particularly low. Similarly, the
10 2013 summer temperature over eastern China was the hottest on record at the time, but it had an estimated
11 recurrence interval of about 4 years in the climate of 2013 (Sun et al., 2014). Furthermore, when other
12 aspects of the risk, vulnerability, and exposure are historically high or have recently increased (see WGII,
13 Chapter 16, Section 16.4), relatively moderate extremes can have very high impacts (Otto et al., 2015b;
14 Philip et al., 2018b). As warming continues, the climate moves further away from its historical state with
15 which we are familiar, resulting in an increased likelihood of unprecedented events and surprises. This is
16 particularly the case under high global warming levels e.g. such as the climate of the late 21st century under
17 high-emissions scenarios (above 4°C of global warming, CC-Box 11.1).
18
19 Another factor highlighted in Section 11.8 and Box 11.4 making events high-impact and difficult to
20 anticipate is that several locations under moderate warming levels could be affected simultaneously, or very
21 repeatedly by different types of extremes (Mora et al., 2018, Gaupp et al., 2019; Vogel et al., 2019). Box
22 11.4 shows that concurrent events at different locations, which can lead to major impacts across the world,
23 can also result from the combination of anomalous circulation or natural variability (ENSO) patterns with
24 amplification of resulting responses to human-induced global warming. Also multivariate extremes at single
25 locations pose specific challenges to anticipation (Section 11.8), with low-likelihoods in the current climate
26 but the probability of occurrence of such compound events strongly increasing with increasing global
27 warming levels (Vogel et al., 2020a). Therefore, in order to estimate whether and at what level of global
28 warming very high impacts arising from extremes would occur, the spatial extent of extremes and the
29 potential of compounding extremes need to be assessed. Sections 11.3, 11.4, 11.7 and 11.8 highlight
30 increasing evidence that temperature extremes, higher intensity precipitation accompanying tropical
31 cyclones, and compound events such as dry/hot conditions conducive to wildfire or storm surges resulting
32 from sea level rise and heavy precipitation events, pose widespread threats to societies already at relatively
33 low warming levels. Studies have already shown that the probability for some recent extreme events is so
34 small in the undisturbed world such that these events may not have been possible without human influence
35 (Section 11.2.4). Box 11.2, Table 1, provides examples of projected changes in LLHI extremes (single
36 extremes, compound events) of potential relevance for impact and adaptation assessments showing that
37 today’s very rare events can become commonplace in a warmer future.
38
39 In summary, the future occurrence of LLHI events linked to climate extremes is generally associated with
40 low confidence, but cannot be excluded, especially at global warming levels above 4°C. Compound events,
41 including concurrent extremes, are a factor increasing the probability of LLHI events (high confidence).
42 With increasing global warming some compound events with low likelihood in past and current climate
43 will become more frequent, and there is a higher chance of historically unprecedented events and
44 surprises (high confidence). However, even extreme events that do not have a particularly low probability
45 in the present climate (at more than 1°C of global warming) can be perceived as surprises because of the
46 pace of global warming (high confidence).
47
48 Box 11.2, Table 1: Examples of changes in LLHI extreme conditions (single extremes, compound events) at different
49 global warming levels
50
+1°C (present- +1.5°C +2°C +3°C and
day) higher
Risk ratio for annual hottest daytime 1 3.3 (i.e. 230% 8.2 (i.e. 720% Not assessed
temperature (TXx) with 1% of probability higher higher
under present-day warming (+1°C) (Kharin et probability) probability)
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al., 2018): Global land
Risk ratio for heavy precipitation events 1 1.2 (i.e. 20% 1.5 (i.e. 50% Not assessed
(Rx1day) with 1% of probability under present- higher higher
day warming (+1°C) (Kharin et al., 2018): probability) probability)
Global land
Risk ratio for 1- 5 day duration extreme floods Up to 3 in Up to 5 in 2-6 in most Up to 12 in
with 1% of probability under present-day individual individual locations individual
warming (+1°C) (Ali et al., 2019a)Indian locations locations locations
subcontinent (4°C)
Probability of “extremes extremes” hot days ~20 days over about ~50 about ~150 about ~500
with 1/1000 probability at the end of 20th 20 years in days in 20 days in 20 days in 20
century (Vogel et al., 2020a): Global land most locations years in most years in most years in most
locations locations locations
(3°C)
Probability of co-occurrence in the same week 0% probability ~1 week ~4-5 weeks >9 weeks
of hot days with 1/1000 probability and dry within 20 within 20 within 20
days with 1/1000 probability at the end of 20th years years years (3°C)
century (Vogel et al., 2020a): Amazon
Projected soil moisture drought duration per 41 days (+46% 58 days 71 days 125 days
year (Samaniego et al., 2018): Mediterranean compared to (+107% (+154% (+346%
region late 20th compared to compared to compared to
century) late 20th late 20th late 20th
century) century) century)
(3°C)
Increase in days exposed to dangerous extreme Not assessed, 1.6 times 2.3 times ~ 80% of
heat (measured in Health Heat Index (HHI) baseline is higher risk of higher risk of land area
(Sun et al., 2019c) global land 1981-2000 experiencing experiencing exposed to
heat > 40.6 heat > 40.6 dangerous
heat, tropical
regions 1/3
of the year
(4°C)
Increase in regional mean fire season length Not assessed, 6.2 days 9.5 days ~ 50 days
(Sun et al., 2019c; Xu et al., 2020) global land baseline is (4°C)
1981-2000
1
2
3 [END BOX 11.2 HERE]
4
5
6 11.2 Data and Methods
7
8 This section provides an assessment of observational data and methods used in the analysis and attribution of
9 climate change specific to weather and climate extremes, and also introduces some concepts used in
10 presenting future projections of extremes in the chapter. The main focus is on extreme events over land, as
11 extremes in the ocean are assessed in Chapter 9 of this Report. Later sections (11.3-11.8) also provide
12 additional assessments on relevant observational datasets and model validation specific for the type of
13 extremes to be assessed. General background on climate modelling is provided in Chapters 4 and 10.
14
15
16 11.2.1 Definition of extremes
17
18 In the literature, an event is generally considered extreme if the value of a variable exceeds (or lies below) a
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1 threshold. The thresholds have been defined in different ways, leading to differences in the meaning of
2 extremes that may share the same name. For example, two sets of frequency of hot/warm days have been
3 used in the literature. One set counts the number of days when maximum daily temperature is above a
4 relative threshold defined as the 90th or higher percentile of maximum daily temperature for the calendar day
5 over a base period. An event based on such a definition can occur during any time of the year and the impact
6 of such an event would differ depending on the season. The other set counts the number of days in which
7 maximum daily temperature is above an absolute threshold such as 35°C, because exceedance of this
8 temperature can sometimes cause health impacts (however, these impacts may depend on location and
9 whether ecosystems and the population are adapted to such temperatures). While both types of hot extreme
10 indices have been used to analyze changes in the frequency of hot/warm events, they represent different
11 events that occur at different times of the year, possibly affected by different types of processes and
12 mechanisms, and possibly also associated with different impacts.
13
14 Changes in extremes have also been examined from two perspectives: changes in the frequency for a given
15 magnitude of extremes or changes in the magnitude for a particular return period (frequency). Changes in the
16 probability of extremes (e.g., temperature extremes) depend on the rarity of the extreme event that is
17 assessed, with a larger change in probability associated with a rarer event (e.g., Kharin et al., 2018). On the
18 other hand, changes in the magnitude represented by the return levels of the extreme events may not be as
19 sensitive to the rarity of the event. While the answers to the two different questions are related, their
20 relevance to different audiences may differ. Conclusions regarding the respective contribution of greenhouse
21 gas forcing to changes in magnitude versus frequency of extremes may also differ (Otto et al., 2012).
22 Correspondingly, the sensitivity of changes in extremes to increasing global warming is also dependent on
23 the definition of the considered extremes. In the case of temperature extremes, changes in magnitude have
24 been shown to often depend linearly on global surface temperature (Seneviratne et al., 2016; Wartenburger et
25 al., 2017), while changes in frequency tend to be non-linear and can, for example, be exponential for
26 increasing global warming levels (Fischer and Knutti, 2015; Kharin et al., 2018). When similar damage
27 occurs once a fixed threshold is exceeded, it is more important to ask a question regarding changes in the
28 frequency. But when the exceedance of this fixed threshold becomes a normal occurrence in the future, this
29 can lead to a saturation in the change of probability (Harrington and Otto, 2018a). On the other hand, if the
30 impact of an event increases with the intensity of the event, it would be more relevant to examine changes in
31 the magnitude. Finally, adaptation to climate change might change the relevant thresholds over time,
32 although such aspects are still rarely integrated in the assessment of projected changes in extremes. Framing,
33 including how extremes are defined and how the questions are asked in the literature, is considered when
34 forming the assessments of this chapter.
35
36
37 11.2.2 Data
38
39 Studies of past and future changes in weather and climate extremes and in the mean state of the climate use
40 the same original sources of weather and climate observations, including in-situ observations, remotely
41 sensed data, and derived data products such as reanalyses. Chapter 2 (Section 2.3) and Chapter 10 (Section
42 10.2) assess various aspects of these data sources and data products from the perspective of their general use
43 and in the analysis of changes in the mean state of the climate in particular. Building on these previous
44 chapters, this subsection highlights particular aspects that are related to extremes and that are most relevant
45 to the assessment of this chapter. The SREX (Chapter 3, Seneviratne et al., 2012) and AR5 (Chapter 2,
46 Hartmann et al., 2013) addressed critical issues regarding the quality and availability of observed data and
47 their relevance for the assessment of changes in extremes.
48
49 Extreme weather and climate events occur on time scales of hours (e.g., convective storms that produce
50 heavy precipitation) to days (e.g., tropical cyclones, heat waves), to seasons and years (e.g., droughts). A
51 robust determination of long-term changes in these events can have different requirements for the spatial and
52 temporal scales and sample size of the data. In general, it is more difficult to determine long-term changes
53 for events of fairly large temporal duration, such as “mega-droughts” that last several years or longer (e.g.,
54 Ault et al. 2014), because of the limitations of the observational sample size. Literature that study changes in
55 extreme precipitation and temperature often use indices representing specifics of extremes that are derived
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1 from daily precipitation and temperature values. Station-based indices would have the same issues as those
2 for the mean climate regarding the quality, availability, and homogeneity of the data. For the purpose of
3 constructing regional information and/or for comparison with model outputs, such as model evaluation, and
4 detection and attribution, these station-based indices are often interpolated onto regular grids. Two different
5 approaches, involving two different orders of operation, have been used in producing such gridded datasets.
6
7 In some cases, such as for the HadEX3 dataset (Dunn et al., 2020), indices of extremes are computed using
8 time series directly derived from stations first and are then gridded over the space. As the indices are
9 computed at the station level, the gridded data products represent point estimates of the indices averaged
10 over the spatial scale of the grid box. In other instances, daily values of station observations are first gridded
11 (e.g., Contractor et al., 2020), and the interpolated values can then be used to compute various indices by the
12 users. Depending on the station density, values for extremes computed from data gridded this way represent
13 extremes of spatial scales anywhere from the size of the grid box to a point. In regions with high station
14 density (e.g., North America, Europe), the gridded values are closer to extremes of area means and are thus
15 more appropriate for comparisons with extremes estimated from climate model output, which is often
16 considered to represent areal means (Chen and Knutson, 2008; Gervais et al., 2014; Avila et al., 2015; Di
17 Luca et al., 2020a). In regions with very limited station density (e.g., Africa), the gridded values are closer to
18 point estimates of extremes. The difference in spatial scales among observational data products and model
19 simulations needs to be carefully accounted for when interpreting the comparison among different data
20 products. For example, the average annual maximum daily maximum temperature (TXx) over land
21 computed from the original ERA-interim reanalysis (at 0.75° resolution) is about 0.4°C warmer than that
22 computed when the ERA-interim dataset is upscaled to the resolution of 2.5° x 3.75° (Di Luca et al., 2020).
23
24 Extreme indices computed from various reanalysis data products have been used in some studies, but
25 reanalysis extreme statistics have not been rigorously compared to observations (Donat et al., 2016a).
26 In general, changes in temperature extremes from various reanalyses were most consistent with gridded
27 observations after about 1980, but larger differences were found during the pre-satellite era (Donat et al.,
28 2014b). Overall, lower agreement across reanalysis datasets was found for extreme precipitation changes,
29 although temporal and spatial correlations against observations were found to be still significant. In regions
30 with sparse observations (e.g., Africa and parts of South America), there is generally less agreement for
31 extreme precipitation between different reanalysis products, indicating a consequence of the lack of an
32 observational constraint in these regions (Donat et al., 2014b, 2016a). More recent reanalyses, such as ERA5
33 (Hersbach et al., 2020), seem to have improved over previous products, at least over some regions (e.g.,
34 Mahto and Mishra, 2019; Gleixner et al., 2020; Sheridan et al., 2020). Caution is needed when reanalysis
35 data products are used to provide additional information about past changes in these extremes in regions
36 where observations are generally lacking.
37
38 Satellite remote sensing data have been used to provide information about precipitation extremes because
39 several products provide data at sub-daily resolution for precipitation (e.g., TRMM; Maggioni et al. 2016)
40 and clouds (e.g., HIMAWARI; Bessho et al., 2016; Chen et al. 2019). However, satellites do not observe the
41 primary atmospheric state variables directly and polar orbiting satellites do not observe any given place at all
42 times. Hence, their utility as a substitute for high-frequency (i.e., daily) ground-based observations is limited.
43 For instance, Timmermans et al. (2019) found little relationship between the timing of extreme daily and
44 five-day precipitation in satellite and gridded station data products over the United States.
45
46
47 [START BOX 11.3 HERE]
48
49 BOX 11.3: Extremes in paleoclimate archives compared to instrumental records
50 Examining extremes in pre-instrumental information can help to put events occurring in the instrumental
51 record (referred to as ‘observed’) in a longer-term context. This box focuses on extremes in the Common Era
52 (CE, the last 2000 years), because there is generally higher confidence in pre-instrumental information
53 gathered from the more recent archives from the Common Era than from earlier evidence. It addresses
54 evidence of extreme events in paleo reconstructions, documentary evidence (such as grape harvest data,
55 religious documents, newspapers, and logbooks) and model-based analyses, and whether observed extremes
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1 have or have not been exceeded in the Common Era. This box provides overviews of i) AR5 assessments
2 and ii) types of evidence assessed here, evidence of iii) droughts, iv) temperature extremes, v) paleofloods,
3 and vi) paleotempests, and vii) a summary.
4
5 AR5 (Chapter 5, Masson-Delmotte et al., 2013) concluded with high confidence that droughts of greater
6 magnitude and of longer duration than those observed in the instrumental period occurred in many regions
7 during the preceding millennium. There was high confidence in evidence that floods during the past five
8 centuries in northern and Central Europe, the western Mediterranean region, and eastern Asia were of a
9 greater magnitude than those observed instrumentally, and medium confidence in evidence that floods in the
10 near East, India and central North America were comparable to modern observed floods. While AR5
11 assessed 20th century summer temperatures compared to those reconstructed in the Common Era, it did not
12 assess shorter duration temperature extremes.
13
14 Many factors affect confidence in information on pre-instrumental extremes. First, the geographical coverage
15 of paleoclimate reconstructions of extremes is not spatially uniform (Smerdon and Pollack, 2016) and
16 depends on both the availability of archives and records, which are environmentally dependent, and also the
17 differing attention and focus from the scientific community. In Australia, for example, the paleoclimate
18 network is sparser than for other regions, such as Asia, Europe and North America, and synthesised products
19 rely on remote proxies and assumptions about the spatial coherence of precipitation between remote climates
20 (Cook et al., 2016c; Freund et al., 2017). Second, pre-instrumental evidence of extremes may be focused on
21 understanding archetypal extreme events, such as the climatic consequences of the 1815 eruption of Mount
22 Tambora, Indonesia (Brohan et al., 2016; Veale and Endfield, 2016). These studies provide narrow evidence
23 of extremes in response to specific forcings (Li, 2017) for specific epochs. Third, natural archives may
24 provide information about extremes in one season only and may not represent all extremes of the same types.
25
26 Evidence of shorter duration extreme event types, such as floods and tropical storms, is further restricted by
27 the comparatively low chronological controls and temporal resolution (e.g., monthly, seasonal, yearly,
28 multiple years) of most archives compared to the events (e.g., minutes to days). Natural archives may be
29 sensitive only to intense environmental disturbances, and so only sporadically record short-duration or small
30 spatial scale extremes. Interpreting sedimentary records as evidence of past short-duration extremes is also
31 complex and requires a clear understanding of natural processes. For example, paleoflood reconstructions of
32 flood recurrence and intensity produced from geological evidence (e.g., river and lake sediments),
33 speleothems (Denniston and Luetscher, 2017), botanical evidence (e.g., flood damage to trees, or tree ring
34 reconstructions), and floral and faunal evidence (e.g., diatom fossil assemblages) require understanding of
35 sediment sources and flood mechanisms. Pre-instrumental records of tropical storm intensity and frequency
36 (also called paleotempest records) derived from overwash deposits of coastal lake and marsh sediments are
37 difficult to interpret. Many factors impact whether disturbances are deposited in archives (Muller et al.,
38 2017) and deposits may provide sporadic and incomplete preservation histories (e.g., Tamura et al., 2018).
39
40 Overall, the most complete pre-instrumental evidence of extremes occurs for long-duration, large-spatial-
41 scale extremes, such as for multi-year meteorological droughts or seasonal- and regional-scale temperature
42 extremes. Additionally, more precise insights into recent extremes emerge where multiple studies have been
43 undertaken, compared to the confidence in extremes reported at single sites or in single studies, which may
44 not necessarily be representative of large-scale changes, or for reconstructions that synthesise multiple
45 proxies over large areas (e.g., drought atlases). Multiproxy synthesis products combine paleoclimate
46 temperature reconstructions and cover sub-continental- to hemispheric-scale regions to provide continuous
47 records of the Common Era (e.g. Ahmed et al., 2013; Neukom et al., 2014 for temperature).
48
49 There is high confidence in the occurrence of long-duration and severe drought events during the Common
50 Era for many locations, although their severity compared to recent drought events differs between locations
51 and the lengths of reconstruction provided. Recent observed drought extremes in some regions (such as the
52 Levant (Cook et al., 2016a), California in the United States (Cook et al., 2014; Griffin and Anchukaitis,
53 2014), and the Andes (Domínguez-Castro et al., 2018)) do not have precedents within the multi-century
54 periods reconstructed in these studies, in terms of duration and/or severity. In some regions (in Southwest
55 North America (Asmerom et al., 2013; Cook et al., 2015), the Great Plains region (Cook et al., 2004), the
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1 Middle East (Kaniewski et al., 2012), and China (Gou et al., 2015), recent drought extremes may have been
2 exceeded in the Common Era. In further locations, there is conflicting evidence for the severity of pre-
3 instrumental droughts compared to observed extremes, depending on the length of the reconstruction and the
4 seasonal perspective provided (see Cook et al., 2016b; Freund et al., 2017 for Australia). There can also be
5 differing conclusions for the severity, or even the occurrence, of specific individual pre-instrumental
6 droughts when different evidence is compared (e.g., Büntgen et al., 2015; Wetter et al., 2014).
7
8 There is medium confidence that the magnitude of large-scale, seasonal-scale extreme high temperatures in
9 observed records exceed those reconstructed over the Common Era in some locations, such as Central
10 Europe. In one example, multiple studies have examined the unusualness of present-day European summer
11 temperature records in a long-term context, particularly in comparison to the exceptionally warm year of
12 1540 CE in Central Europe. Several studies indicate recent extreme summers (2003 and 2010) in Europe
13 have been unusually warm in the context of the last 500 years (Barriopedro et al., 2011; Wetter and Pfister,
14 2013; Wetter et al., 2014; Orth et al., 2016a), or longer (Luterbacher et al., 2016). Others studies show
15 summer temperatures in Central Europe in 1540 were warmer than the present-day (1966–2015) mean, but
16 note that it is difficult to assess whether or not the 1540 summer was for its part warmer than observed
17 record extreme temperatures (Orth et al., 2016a).
18
19 There is high confidence that the magnitude of floods over the Common Era has exceeded observed records
20 in some locations, including Central Europe and eastern Asia. Recent literature supports the AR5
21 assessments (Masson-Delmotte et al., 2013) of floods. High temporally resolved records provide evidence,
22 for example, of Common Era floods exceeding the probable maximum flood levels in the Upper Colorado
23 River, USA (Greenbaum et al., 2014) and peak discharges that are double gauge levels along the middle
24 Yellow River, China (Liu et al., 2014). Further studies demonstrate pre-instrumental or early instrumental
25 differences in flood frequency compared to the instrumental period, including reconstructions of high and
26 low flood frequency in the European Alps (e.g., Swierczynski et al., 2013; Amann et al., 2015) and
27 Himalayas (Ballesteros Cánovas et al., 2017). The combination of extreme historical flood episodes
28 determined from documentary evidence also increases confidence in the determination of flood frequency
29 and magnitude, compared to using geomorphological archives alone (Kjeldsen et al., 2014). In regions, such
30 as Europe and China, that have rich historical flood documents, there is strong evidence of high magnitude
31 flood events over pre-instrumental periods (Benito et al., 2015; Kjeldsen et al., 2014; Macdonald and
32 Sangster, 2017). A key feature of paleoflood records is variability in flood recurrence at centennial
33 timescales (Wilhelm et al., 2019), although constraining climate-flood relationships remains challenging.
34 Pre-instrumental floods often occurred in considerably different contexts in terms of land use, irrigation, and
35 infrastructure, and may not provide direct insight into modern river systems, which further prevents long-
36 term assessments of flood changes being made based on these sources.
37
38 There is medium confidence that periods of both more and less tropical cyclone activity (frequency or
39 intensity) than observed occurred over the Common Era in many regions. Paleotempest studies cover a
40 limited number of locations that are predominantly coastal, and hence provide information on specific
41 locations that cannot be extrapolated basin-wide (see Muller et al., 2017). In some locations, such as the Gulf
42 of Mexico and the New England coast, similarly intense storms to those observed recently have occurred
43 multiple times over centennial timescales (Donnelly et al., 2001; Bregy et al., 2018). Further research
44 focused on the frequency of tropical storm activity. Extreme storms occurred considerably more frequently
45 in particular periods of the Common Era, compared to the instrumental period in northeast Queensland,
46 Australia (Nott et al., 2009; Haig et al., 2014), and the Gulf Coast (e.g., Brandon et al., 2013; Lin et al.,
47 2014).
48
49 The probability of finding an unprecedented extreme event increases with an increased length of past record-
50 keeping, in the absence of longer-term trends. Thus, as a record is extended to the past based on paleo-
51 reconstruction, there is a higher chance of very rare extreme events having occurred at some time prior to
52 instrumental records. Such an occurrence is not, in itself, evidence of a change, or lack of a change, in the
53 magnitude or the likelihood of extremes in the past or in the instrumental period at regional and local scales.
54 Yet, the systematic collection of paleoclimate records over wide areas may provide evidence of changes in
55 extremes. In one study, extended evidence of the last millennium from observational data and paleoclimate
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1 reconstructions using tree rings indicates human activities affected the worldwide occurrence of droughts as
2 early as the beginning of the 20th century (Marvel et al., 2019).
3
4 In summary, there is low confidence in overall changes in extremes derived from paleo-archives. The most
5 robust evidence is high confidence that high-duration and severe drought events occurred at many locations
6 during the last 2000 years. There is also high confidence that high-magnitude flood events occurred at some
7 locations during the last 2000 years, but overall changes in infrastructure and human water management
8 make the comparison with present-day records difficult. But these isolated paleo-drought and paleo-flood
9 events are not evidence of a change, or lack of a change, in the magnitude or the likelihood of relevant
10 extremes.
11
12 [END BOX 11.3 HERE]
13
14
15 11.2.3 Attribution of extremes
16
17 Attribution science concerns the identification of causes for changes in characteristics of the climate system
18 (e.g., trends, single extreme events). A general overview and summary of methods of attribution science is
19 provided in the Cross-Working Group Box 1.1 (in Chapter 1). Trend detection using optimal fingerprinting
20 methods is a well-established field, and has been assessed in the AR5 (Chapter 10, Bindoff et al., 2013), and
21 Chapter 3 in this Report (Section 3.2.1). There are specific challenges when applying optimal fingerprinting
22 to the detection and attribution of trends in extremes and on regional scales where the lower signal-to-noise
23 ratio is a challenge. In particular, the method generally requires the data to follow a Normal (Gaussian)
24 distribution, which is often not the case for extremes. Recent studies showed that extremes can, however, be
25 transformed to a Gaussian distribution, for example by averaging over space, so that optimal fingerprinting
26 techniques can still be used (Zhang et al., 2013; Wen et al., 2013; and Wan et al., 2019). Non-stationary
27 extreme value distributions, which allow for the detailed detection and attribution of regional trends in
28 temperature extremes, have also been used (Wang et al., 2017c).
29
30 Apart from the detection and attribution of trends in extremes, new approaches have been developed to
31 answer the question of whether and to what extent external drivers have altered the probability and intensity
32 of an individual extreme event (NASEM, 2016). In AR5, there was an emerging consensus that the role of
33 external drivers of climate change in specific extreme weather events could be estimated and quantified in
34 principle, but related assessments were still confined to particular case studies, often using a single model,
35 and typically focusing on high-impact events with a clear attributable signal.
36
37 However, since AR5, the attribution of extreme weather events has emerged as a growing field of climate
38 research with an increasing body of literature (see series of supplements to the annual State of the Climate
39 report (Peterson et al., 2012, 2013b, Herring et al., 2014, 2015, 2016, 2018), including the number of
40 approaches to examining extreme events (described in Easterling et al., 2016; Otto, 2017; Stott et al., 2016)).
41 A commonly-used approach, often called the risk-based approach in the literature and referred to here as the
42 “probability-based approach”, produces statements such as ‘anthropogenic climate change made this event
43 type twice as likely’ or ‘anthropogenic climate change made this event 15% more intense’. This is done by
44 estimating probability distributions of the index characterizing the event in today’s climate, as well as in a
45 counterfactual climate, and either comparing intensities for a given occurrence probability (e.g., 1-in-100
46 year event) or probabilities for a given magnitude (see FAQ 11.3). There are a number of different analytical
47 methods encompassed in the probability-based approach building on observations and statistical analyses
48 (e.g., van Oldenborgh et al., 2012), optimal fingerprint methods (Sun et al., 2014), regional climate and
49 weather forecast models (e.g., Schaller et al., 2016), global climate models (GCMs) (e.g., Lewis and Karoly,
50 2013), and large ensembles of atmosphere-only GCMs (e.g., Lott et al., 2013). A key component in any
51 event attribution analysis is the level of conditioning on the state of the climate system. In the least
52 conditional approach, the combined effect of the overall warming and changes in the large-scale atmospheric
53 circulation are considered and often utilize fully coupled climate models (Sun et al., 2014). Other more
54 conditional approaches involve prescribing certain aspects of the climate system. These range from
55 prescribing the pattern of the surface ocean change at the time of the event (e.g. Hoerling et al., 2013, 2014),
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1 often using AMIP-style global models, where the choice of sea surface temperature and ice patterns
2 influences the attribution results (Sparrow et al., 2018), to prescribing the large-scale circulation of the
3 atmosphere and using weather forecasting models or methods (e.g., Pall et al., 2017; Patricola and Wehner,
4 2018; Wehner et al., 2018a). These highly conditional approaches have also been called “storylines”
5 (Shepherd, 2016; Cross-Working Group Box 1.1 in Chapter 1) and can be useful when applied to extreme
6 events that are too rare to otherwise analyse or where the specific atmospheric conditions were central to the
7 impact. These methods are also used to enable the use of very-high-resolution simulations in cases were
8 lower-resolution models do not simulate the regional atmospheric dynamics well (Shepherd, 2016; Shepherd
9 et al., 2018). However, the imposed conditions limit an overall assessment of the anthropogenic influence on
10 an event, as the fixed aspects of the analysis may also have been affected by climate change. For instance,
11 the specified initial conditions in the highly conditional hindcast attribution approach often applied to
12 tropical cyclones (e.g., Patricola and Wehner, 2018; Takayabu et al., 2015) permit only a conditional
13 statement about the magnitude of the storm if similar large-scale meteorological patterns could have
14 occurred in a world without climate change, thus precluding any attribution statement about the change in
15 frequency if used in isolation. Combining conditional assessments of changes in the intensity with a multi-
16 model approach does allow for the latter as well (Shepherd, 2016).
17
18 The outcome of event attribution is dependent on the definition of the event (Leach et al., 2020), as well as
19 the framing (Christidis et al., 2018; Jézéquel et al., 2018; Otto et al., 2016) and uncertainties in observations
20 and modelling. Observational uncertainties arise both in estimating the magnitude of an event as well as its
21 rarity (Angélil et al., 2017). Results of attribution studies can also be very sensitive to the choice of climate
22 variables (Sippel and Otto, 2014; Wehner et al., 2016). Attribution statements are also dependent on the
23 spatial (Uhe et al., 2016; Cattiaux and Ribes, 2018; Kirchmeier‐Young et al., 2019) and temporal
24 (Harrington, 2017; Leach et al., 2020) extent of event definitions, as events of different scales involve
25 different processes (Zhang et al., 2020d) and large-scale averages generally yield higher attributable changes
26 in magnitude or probability due to the smoothing out of the noise. In general, confidence in attribution
27 statements for large-scale heat and lengthy extreme precipitation events have higher confidence than shorter
28 and more localized events, such as extreme storms, an aspect also relevant for determining the emergence of
29 signals in extremes or the confidence in projections (see also Cross-Chapter Box Atlas.1)
30
31 The reliability of the representation of the event in question in the climate models used in a study is essential
32 (Angélil et al., 2016; Herger et al., 2018). Extreme events characterized by atmospheric dynamics that stretch
33 the capabilities of current-generation models (see Section 10.3.3.4, Shepherd, 2014; Woollings et al., 2018)
34 limit the applicability of the probability-based approach of event attribution. The lack of model evaluation, in
35 particular in early event attribution studies, has led to criticism of the emerging field of attribution science as
36 a whole (Trenberth et al., 2015) and of individual studies (Angélil et al., 2017). In this regard, the storyline
37 approach (Shepherd, 2016) provides an alternative option that does not depend on the model’s ability to
38 represent the circulation reliably. In addition, several ways of quantifying statistical uncertainty (Paciorek et
39 al., 2018) and model evaluation (Lott and Stott, 2016; Philip et al., 2018b, 2020) have been employed to
40 evaluate the robustness of event attribution results. For the unconditional probability-based approach, multi-
41 model and multi-approach (e.g., combining observational analyses and model experiments) methods have
42 been used to improve the robustness of event attribution (Hauser et al., 2017; Otto et al., 2018a; Philip et al.,
43 2018b, 2019, 2020; van Oldenborgh et al., 2018; Kew et al., 2019).
44
45 In the regional tables provided in Section 11.9, the different lines of evidence from event attribution studies
46 and trend attributions are assessed alongside one another to provide an assessment of the human contribution
47 to observed changes in extremes in all AR6 regions .
48
49
50 11.2.4 Projecting changes in extremes as a function of global warming levels
51
52 The most important quantity used to characterize past and future climate change is global warming relative
53 to its pre-industrial level. On the one hand, changes in global warming are linked quasi-linearly to global
54 cumulative CO2 emissions (IPCC, 2013). On the other hand, changes in regional climate, including many
55 types of extremes, scale quasi-linearly with changes in global warming, often independently of the
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1 underlying emissions scenarios (SR15 Ch3; Seneviratne et al., 2016; Wartenburger et al., 2017; Matthews et
2 al., 2017; Tebaldi and Knutti 2018, Sun et al., 2018a, Kharin et al., 2018, Beusch et al., 2020b; Li et al.,
3 2020). Finally, the use of global warming levels in the context of global policy documents (in particular the
4 2015 Paris Agreement, UNFCCC 2015), implies that information on changes in the climate system, and in
5 particular extremes, as a function of global warming are of particular policy relevance. Cross-Chapter Box
6 11.1 provides on overview on the translation between information at global warming levels (GWLs) and
7 scenarios.
8
9 The assessment of projections of future changes in extremes as function of GWL has an advantage in
10 separating uncertainty associated with the global warming response (see Chapter 4) from the uncertainty
11 resulting from the regional climate response as a function of GWLs (Seneviratne and Hauser, 2020). If the
12 interest is in the projection of regional changes at certain GWLs, such as those defined by the Paris
13 Agreement, projections based on time periods and emission scenarios have unnecessarily larger uncertainty
14 due to differences in model global transient climate responses. To take advantage of this feature and to
15 provide easy comparison with SR15, assessments of projected changes in this chapter are largely provided in
16 relation to future GWLs, with a focus on changes at +1.5°C, +2°C, and +4°C of global warming above pre-
17 industrial levels (e.g. Tables 11.1, 11.2 and regional tables in Section 11.9). These encompass a scenario
18 compatible with the aim of the Paris Agreement (+1.5°C), a scenario slightly overshooting the aims of the
19 Paris Agreement (+2°C), and a “worst-case” scenario with no mitigation (+4°C). The CC-Box 11.1 provides
20 a background on the GWL sampling approach used in the AR6, both for the computation of GWL
21 projections from ESMs contributing to the 6th Phase of the Coupled Model Intercomparison Project (CMIP6)
22 as well as for the mapping of existing scenario-based literature for CMIP6 and the 5th Phase of CMIP
23 (CMIP5) to assessments as function of GWLs (see also Section 11.9. and Table 11.3 for an example).
24
25 While regional changes in many types of extremes do scale robustly with global surface temperature,
26 generally irrespective of emission scenarios (Section 11.1.4; Figures 11.3, 11.6, 11.7; CC-Box 11.1), effects
27 of local forcing can distort this relation. In particular, emission scenarios with the same radiative forcing can
28 have different regional extreme precipitation responses resulting from different aerosol forcing (Wang et al.,
29 2017d). Another example is related to forcing from land use and land cover changes (Section 11.1.6).
30 Climate models often either overestimate or underestimate observed changes in annual maximum daily
31 maximum temperature depending on the region and considered models (Donat et al., 2017; Vautard et al.,
32 9999). Part of the discrepancies may be due to the lack of representation of some land forcings, in particular
33 crop intensification and irrigation (Mueller et al., 2016b; Thiery et al., 2017; Findell et al., 2017; Thiery et
34 al., 2020). Since these local forcings are not represented and their future changes are difficult to project,
35 these can be important caveats when using GWL scaling to project future changes for these regions.
36 However, these caveats also apply to the use of scenario-based projections.
37
38 SR15 (Chapter 3) assessed different climate responses at +1.5°C of global warming, including transient
39 climate responses, short-term stabilization responses, and long-term equilibrium stabilization responses, and
40 their implications for future projections of different extremes. Indeed, the temporal dimension, that is, when
41 the given GWL occurs, also matters for projections, in particular beyond the 21st century and for some
42 climate variables with large inertia (e.g., sea level rise and associated extremes). Nonetheless, for
43 assessments focused on conditions within the next decades and for the main extremes considered in this
44 chapter, derived projections are relatively insensitive to details of climate scenarios and can be well
45 estimated based on transient simulations (CC-Box 11.1; see also SR15).
46
47 An important question is the identification of the GWL at which a given change in a climate extreme can
48 begin to emerge from climate noise. Figure 11.8 displays analyses of the GWLs at which emergence in hot
49 extremes (20-year return values of TXx, TXx_20yr) and heavy precipitation (20-year return values of
50 Rx1day, Rx1day_20yr) is identified in AR6 regions for the whole CMIP5 and CMIP6 ensembles). Overall,
51 signals for extremes emerge very early for TXx_20yr, already below 0.2°C in many regions (Fig. 11.8a,b),
52 and at around 0.5°C in most regions. This is consistent with conclusions from the SR15 Ch3 for less-rare
53 temperature extremes (TXx on the yearly time scale), which shows that a difference as small as 0.5°C of
54 global warming, e.g. between +1.5°C and +2°C of global warming, leads to detectable differences in
55 temperature extremes in TXx in most WGI AR6 regions in CMIP5 projections (e.g.,Wartenburger et al.,
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Final Government Distribution Chapter11 IPCC AR6 WGI
1 2017; Seneviratne et al., 2018b). The GWL emergence for Rx1day_20yr is also largely consistent with
2 analyses for less-extreme heavy precipitation events (Rx5day on the yearly time scale) in the SR15 (see
3 Chapter 3).
4
5 To some extent, analyses as functions of GWLs replace the time axis with a global surface temperature axis.
6 Nonetheless, information on the timing of given changes in extremes is obviously also relevant. Regarding
7 this information, that is, the time frame at which given global warming levels are reached, the readers are
8 referred to Chapter 4 (Section 4.6; see also CC-Box 11.1). Figure 11.5 provides a synthesis of attributed and
9 projected changes in extremes as function of GWLs (see also Figs. 11.3, 11.6, and 11.7 for regional
10 analyses).
11
12
13 [START FIGURE 11.8 HERE]
14
15 Figure 11.8: Global and regional-scale emergence of changes in temperature (a) and precipitation (b) extremes for the
16 globe (glob.), global oceans (oc.), global lands (land), and the AR6 regions. Colours indicate the multi-
17 model mean global warming level at which the difference in 20-year means of the annual maximum daily
18 maximum temperature (TXx) and the annual maximum daily precipitation (Rx1day) become significantly
19 different from their respective mean values during the 1851–1900 base period. Results are based on
20 simulations from the CMIP5 and CMIP6 multi-model ensembles. See Atlas.1.3.2 for the definition of
21 regions. Adapted from Seneviratne and Hauser, 2020) under the terms of the Creative Commons
22 Attribution license.
23
24 [END FIGURE 11.8 HERE]
25
26
27 [START CROSS-CHAPTER BOX 11.1 HERE]
28
29 Cross-Chapter Box 11.1: Translating between regional information at global warming levels vs
30 scenarios for end users
31
32 Contributors: Erich Fischer (Switzerland), Mathias Hauser (Switzerland), Sonia I. Seneviratne
33 (Switzerland), Richard Betts (UK), José M. Gutiérrez (Spain), Richard G. Jones (UK), June-Yi Lee
34 (Republic of Korea), Malte Meinshausen (Australia/Germany), Friederike Otto (UK/Germany), Izidine Pinto
35 (Mozambique), Roshanka Ranasinghe (The Netherlands/Sri Lanka/Australia), Joeri Rogelj
36 (Germany/Belgium), Bjørn Samset (Norway), Claudia Tebaldi (USA), Laurent Terray (France)
37
38 Background
39 Traditionally, projections of climate variables are summarized and communicated as function of time and
40 scenario. Recently, quantifying global and regional climate at specific global warming levels (GWLs) has
41 become widespread, motivated by the inclusion of explicit GWLs in the long-term temperature goal of the
42 Paris Agreement (Section 1.6.2). GWLs, expressed as changes in global surface temperature relative to the
43 1850-1900 period (see CCBox 2.3), are used in the SR15 and in the assessment of Reasons for Concerns in
44 the WGII reports (see also CCBox 12.1). CCB 11.1, Figure 1 illustrates how the assessment of the climate
45 response at GWLs relates to the uncertainty in scenarios regarding the timing of the respective GWLs, as
46 well as to the uncertainty in the associated regional climate responses, including extremes and other climatic
47 impact-drivers (CIDs). For many (but not all) climate variables and CIDs the response pattern for a given
48 GWL is consistent across different scenarios (Chapters 1, 4, 9, 11 and Atlas). GWLs are defined as long-
49 term means (e.g. 20-year averages) compared to the pre-industrial period, are commonly used in the
50 literature and were also underlying main assessments of SR15 (Chapter 3).
51
52
53 [START CROSS-CHAPTER BOX 11.1, FIGURE 1 HERE]
54
55 Cross-Chapter Box 11.1, Figure 1: Schematic representation of relationship between emission scenarios, global
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1 warming levels (GWLs), regional climate responses, and impacts. The
2 illustration shows the implied uncertainty problem associated with differentiating
3 between 1.5, 2°C, and other GWLs. Focusing on GWL raises questions
4 associated with emissions pathways to get to these temperatures (scenarios), as
5 well as questions associated with regional climate responses and the associated
6 impacts at the corresponding GWL (the impacts question). Adapted from (James
7 et al., 2017) and (Rogelj, 2013) under the terms of the Creative Commons
8 Attribution license.
9
10 [END CROSS-CHAPTER BOX 11.1, FIGURE 1 HERE]
11
12
13 Numerous studies have compared the regional response to anthropogenic forcing at GWLs in annual and
14 seasonal mean values and extremes of different climate and impact variables across different multi-model
15 ensembles and/or different scenarios (e.g. Frieler et al., 2012; Schewe et al., 2014; Schleussner et al., 2016;
16 Seneviratne et al., 2016; Wartenburger et al., 2017; Dosio and Fischer, 2018; Tebaldi et al., 2020; (Herger et
17 al., 2015; Betts et al., 2018; Samset et al., 2019), see Sections 4.6.1, 8.5.3, 9.3.1, 9.5, 9.6.3, 10.4.3 and 11.2.4
18 for further details). The regional response patterns at given GWLs have been found to be consistent across
19 different scenarios for many climate variables (CC-Box 11.1 Fig.2) (Pendergrass et al., 2015; Seneviratne et
20 al., 2016; Wartenburger et al., 2017; Seneviratne and Hauser, 2020). The consistency tends to be higher for
21 temperature-related variables than for variables in the hydrological cycle or variables characterizing
22 atmospheric dynamics, and for intermediate to high emission scenarios than for low-emission scenarios (e.g.
23 for mean precipitation in the RCP2.6 scenario: Pendergrass et al., 2015; Wartenburger et al., 2017).
24 Nonetheless, CCB 11.1 Figure 2 illustrates that even for mean precipitation, which is known to be forcing-
25 dependent (Section 4.6.1 and Section 8.5.3), scenario differences in the response pattern at a given GWL are
26 smaller than model uncertainty and internal variability in many regions (Herger et al., 2015). The response
27 pattern is further found to be broadly consistent between models that reach a GWL relatively early and those
28 that reach it later under a given SSP (see CC Box 11.1 Fig.2 g, h)
29
30
31 [START CROSS-CHAPTER 11.1, FIGURE 2 HERE]
32
33 Cross-Chapter Box 11.1, Figure 2: (a-c) CMIP6 multi-model mean precipitation change at 2°C GWL (20-yr
34 mean) in three different SSP scenarios relative to 1850-1900. All models reaching
35 the corresponding GWL in the corresponding scenario are averaged. The number
36 of models averaged across is shown at the top right of the panel. The maps for the
37 other two SSP scenarios SSP1-1.9 (five models only) and SSP3-7.0 (not shown)
38 are consistent. (d-f) Same as (a-c) but for annual mean temperature. (g) Annual
39 mean temperature change at 2°C in CMIP6 models with high warming rate
40 reaching the GWL in the corresponding scenario before the earliest year of the
41 assessed very likely range (section 4.3.4) (h) Climate response at 2°C GWL across
42 all SSP1-1.9, SSP2-2.6, SSP2-4.5. SSP3-7.0 and SSP5-8.5 in all other models not
43 shown in (g). The good agreement of (g) and (h) demonstrate that the mean
44 temperature response at 2°C is not sensitive to the rate of warming and thereby the
45 GSAT warming of the respective models in 2081-2100. Uncertainty is represented
46 using the advanced approach: No overlay indicates regions with robust signal,
47 where ≥66% of models show change greater than variability threshold and ≥80%
48 of all models agree on sign of change; diagonal lines indicate regions with no
49 change or no robust signal, where <66% of models show a change greater than the
50 variability threshold; crossed lines indicate regions with conflicting signal, where
51 ≥66% of models show change greater than variability threshold and <80% of all
52 models agree on sign of change. For more information on the advanced approach,
53 please refer to the Cross-Chapter Box Atlas.1.
54
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1 [END CROSS-CHAPTER BOX 11.1, FIGURE 2 HERE]
2
3
4 In contrast to linear pattern scaling (Mitchell, 2003; Collins et al., 2013a), the use of GWLs as a dimension
5 of integration does not require linearity in the response of a climate variable. It is thus even useful for metrics
6 which do not show a linear response, such as the frequency of heat extremes over land and oceans (Fischer
7 and Knutti, 2015; Perkins-Kirkpatrick and Gibson, 2017; Frölicher et al., 2018; Kharin et al., 2018) if the
8 relationship of the variable of interest to the GWL is scenario independent. The latter means that the
9 response is independent of the pathway and relative contribution of various radiative forcings. For some
10 more complex indices like warm-spell duration or for regions with strong aerosol changes, discrepancies can
11 be larger (Wang et al., 2017d; King et al., 2018; Tebaldi et al., 2020) (see also subsection below on GWLs vs
12 scenarios for further caveats).
13
14 The limited scenario dependence of the GWL-based response for many variables implies that the regional
15 response to emissions scenarios can be split in almost independent contributions of 1) the transient global
16 warming response to scenarios (see Chapter 4), and 2) the regional response as function of a given GWL,
17 which has also been referred to as “regional climate sensitivity” (Seneviratne and Hauser, 2020). This
18 property has also been used to develop regionally-resolved emulators for global climate models, using global
19 surface temperature as input (Beusch et al., 2020; Tebaldi et al., 2020). Analyses of the CMIP6 and CMIP5
20 multi-model ensembles shows that the GWL-based responses are very similar for temperature and
21 precipitation extremes across the ensembles (Li et al., 2020a; Seneviratne and Hauser, 2020; Wehner, 2020).
22 This is despite their difference in global warming response (Chapter 4), confirming a substantial decoupling
23 between the two responses (global warming vs GWL-based regional response) for these variables. Thus, the
24 GWL approach isolates the uncertainty in the regional climate response from the global warming uncertainty
25 induced by scenario, global mean model response and internal variability (CCB Figure 1).
26
27 Mapping between GWL- and scenario-based responses in model analyses
28 To map scenario-based climate projections into changes at specific GWLs, first, all individual ESM
29 simulations that reach a certain GWL are identified. Second, the climate response patterns at the respective
30 GWL are calculated using an approach termed here “GWL-sampling approach” – sometimes also referred to
31 as epoch analysis, time shift, or time sampling approach –, taking into account all models and scenarios
32 (CCB Figure 3). Note that the range of years when a given GWL is reached in the CMIP6 ensemble is
33 different from the AR6 assessed range of projected global surface temperature (Table 4.5; Section 4.3.4).
34 The latter further takes into account different lines of evidence, including the assessed observed warming
35 between pre-industrial and present day, information from observational constraints on CMIP6, and emulators
36 using the assessed transient climate response (TCR) and equilibrium climate sensitivity (ECS) ranges
37 (Section 4.3.4). Hence the Chapter 4 assessed range (Table 4.5) is the reference to determine when a given
38 GWL is likely reached under given scenarios, while the mapping between scenarios/time frames and GWLs
39 is used to assess the respective regional responses happening at these time frames (which also allows to
40 account for the global surface temperature assessment rather than using scenarios analyses directly from
41 CMIP6 output).
42
43 In the model-based asssessment of Chapters 4, 8, 10, 11, 12 and the Atlas, the estimation of changes at
44 GWLs are generally defined as the 20-year time period in which the mean global surface air temperature
45 (GSAT; CCBox 2.3) first exceeds a certain anomaly relative to 1850-1900 (for simulations that start after
46 1850, relative to all years up to 1900 CCB Figure 3). The years when each individual model reaches a given
47 GWL for CMIP6 and CMIP5 can be found in Hauser et al. (2021). The changes at given GWLs are
48 identified for each ensemble member (for all scenarios) individually. Thereby, a given GWL is potentially
49 reached a few years earlier or later in different realizations of the same model due to internal variability, but
50 the temperature averaged across the 20-year period analysed in any simulation is consistent with the GWL.
51 Instead of blending the information from the different scenarios, the Interactive Atlas can be used to compare
52 the GWL spatial patterns and timings across the different scenarios (see Section Atlas 1.3.1).
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1
2
3 [START CROSS-CHAPTER BOX 11.1, FIGURE 3 HERE]
4
5 Cross-Chapter Box11.1, Figure 3: Illustration of the AR6 GWL sampling approach to derive the timing and the
6 response at a given GWL for the case of CMIP6 data. For the mapping of
7 scenarios/time slices into GWLs for CMIP6, please refer to Table 4.2. Respective
8 numbers for the CMIP6 multi-model experiment are provided in the Chapter 11
9 Supplementary Material (11.SM.1). Note that the time frames used to derived the
10 GWL time slices can also include different number of years (e.g. 30 years for
11 some analyses).
12
13
14 [END CROSS-CHAPTER BOX 11.1, FIGURE 3 HERE]
15
16
17 Mapping between GWL- and scenario-based responses for literature
18 A large fraction of the literature considers scenario-based analyses for given time slices. When GWL-based
19 information is required instead, an approximated mapping of the multi-model mean can be derived based on
20 the known GWL in the given experiments for a particular time period. As a rough approximation, CMIP6
21 multi-model mean projections for the near-term (2021-2040) correspond to changes at about 1.5°C, and
22 projections for the high-end scenario (SSP5-8.5) for the long-term (2081-2100) correspond to about 4-5°C of
23 global warming (see Table 4.2 for changes in the CMIP6 ensemble and the Chapter 11 Supplementary
24 Material (11.SM.1) and Hauser (2021) for details on other time periods and CMIP5). These approximated
25 changes are for instance used for some of the GWL-based assessments provided in the Chapter 11 regional
26 tables (Section 11.9; Table 11.3) when literature based on scenario projections is used to assessed estimated
27 changes at given GWLs.
28
29 GWLs vs scenarios
30 The use of scenarios remains a key element to inform mitigation decisions (Chapter 1, CCB1.4), to assess
31 which emission pathways are consistent with a certain GWL (CCB1.4 Figure1), to estimate when certain
32 GWLs are reached (Section 4.3.4), and to assess for which variables it is meaningful to use GWLs as a
33 dimension of integration. The use of scenarios is also essential for variables whose climate response strongly
34 depends on the contribution of radiative forcing (e.g. aerosols) and land use and land management changes,
35 and are time and warming rate dependent (e.g. sea level rise), or differ between transient and quasi-
36 equilibrium states. Furthermore, the use of concentration or emission-driven scenario simulations is required
37 if regional climate assessments need to account for the uncertainty in GSAT changes or climate-carbon
38 feedbacks.
39
40 Forcing dependence of the GWL response is found for global mean precipitation (Section 8.4.3), but less for
41 regional patterns of mean precipitation changes (CC-Box 11.1, Fig. 2). Limited dependence is found for
42 extremes, as highlighted above. In the cryosphere, elements that are quick to respond to warming like sea ice
43 area, permafrost, and snow show little scenario dependence (Chapter 9.3.1.1, 9.5.2.3, 9.5.3.3), whereas slow-
44 responding variables such as ice volumes of glaciers and ice sheets respond with a substantial delay and due
45 to their inertia, the response depends on when a certain GWL is reached. This also applies to some extent for
46 sea level rise where, for example, the contributions of melting glaciers and ice sheets depend on the pathway
47 followed to reach a given GWL (Chapter 9.6.3.4).
48
49 In addition to the lagged effect, the climate response at a given GWL may differ before and after a period of
50 overshoot, for example in the Atlantic Meridional Overturning Circulation (e.g. Palter et al. 2018). Finally,
51 as assessed in IPCC SR15, there is a difference in the response even for temperature-related variables if a
52 GWL is reached in a rapidly warming transient state or in an equilibrium state when the land-sea warming
53 contrast is less pronounced (e.g. King et al. 2020). However, in this report GWLs are used in the context of
54 projections for the 21st century when the climate response is mostly not in equilibrium and where projections
55 for many variables are less dependent on the pathway than for projections beyond 2100 (Section 9.6.3.4).
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1
2 Key conclusions on assessments based on GWLs
3
4 GWL-based projections can inform society and policymakers on how climate would change under GWLs
5 consistent with the aims of the Paris Agreement (stabilization at 1.5°C/well below 2°C), as well as on the
6 consequences of missing these aims and reaching GWLs of 3°C or 4°C by the end of the century. The AR6
7 assessment shows that every bit of global warming matters and that changes in global warming of 0.5°C lead
8 to statistically significant changes in mean climate and climate extremes on global scale and for large regions
9 (Sections 4.6.2, 11.2.4, 11.3, 11.4, 11.6, 11.9; Figs 11.8, 11.9, Atlas, Interactive Atlas), as also assessed in
10 the IPCC SR15.
11
12
13 [END CROSS-CHAPTER BOX 11.1 HERE]
14
15
16 11.3 Temperature extremes
17
18 This section assesses changes in temperature extremes at global, continental and regional scales. The main
19 focus is on the changes in the magnitude and frequency of moderate extreme temperatures (those that occur
20 several times a year) to very extreme temperatures (those that occur once-in-10-years or longer) of time
21 scales from a day to a season, though there is a strong emphasis on the daily scale where literature is most
22 concentrated.
23
24
25 11.3.1 Mechanisms and drivers
26
27 The SREX (IPCC, 2012) and AR5 (IPCC, 2014) concluded that greenhouse gas forcing is the dominant
28 factor for the increases in the intensity, frequency, and duration of warm extremes and the decrease in those
29 of cold extremes. This general global-scale warming is modulated by large-scale atmospheric circulation
30 patterns, as well as by feedbacks such as soil moisture-evapotranspiration-temperature and snow/ice-albedo-
31 temperature feedbacks, and local forcings such as land use change or changes in aerosol concentrations at the
32 regional and local scales (Box 11.1, Sections 11.1.5, 11.1.6). Therefore, changes in temperature extremes at
33 regional and local scales can have heterogeneous spatial distributions. Changes in the magnitudes (or
34 intensities) of extreme temperatures are often larger than changes in global surface temperature, because of
35 larger warming on land than on the ocean surface (2.3.1.1) and feedbacks, though they are of similar
36 magnitude to changes in the local mean temperature (Fig 11.2).
37
38 Extreme temperature events are associated with large-scale meteorological patterns (Grotjahn et al., 2016).
39 Quasi-stationary anticyclonic circulation anomalies or atmospheric blocking events are linked to temperature
40 extremes in many regions, such as in Australia (Parker et al., 2014; Perkins-Kirkpatrick et al., 2016), Europe
41 (Brunner et al., 2017, 2018; Schaller et al., 2018), Eurasia (Yao et al., 2017), Asia (Chen et al., 2016; Ratnam
42 et al., 2016; Rohini et al., 2016), and North America (Yu et al., 2018, 2019b; Zhang and Luo, 2019). Mid-
43 latitude planetary wave modulations affect short-duration temperature extremes such as heat waves (Perkins,
44 2015; Kornhuber et al., 2020). The large-scale modes of variability (Annex VI) affect the strength,
45 frequency, and persistence of these meteorological patterns and, hence, temperature extremes. For example,
46 cold and warm extremes in the mid-latitudes are associated with atmospheric circulation patterns such as the
47 Pacific-North American (PNA) pattern, as well as atmosphere-ocean coupled modes such as Pacific Decadal
48 Variability (PDV), the North Atlantic Oscillation (NAO), and Atlantic Multidecadal Variability (AMV)
49 (Kamae et al., 2014; Johnson et al., 2018; Ruprich-Robert et al., 2018; Yu et al., 2018, 2019a; Müller et al.,
50 2020; Section 11.1.5). Changes in the modes of variability in response to warming would therefore affect
51 temperature extremes (Clark and Brown, 2013; Horton et al., 2015). The level of confidence in those
52 changes, both in the observations and in future projections, varies, affecting the level of confidence in
53 changes in temperature extremes in different regions. As highlighted in Chapters 2-4 of this Report, it is
54 likely that there have been observational changes in the extratropical jets and mid-latitude jet meandering
55 (Section 2.3.1.4.3; Cross-Chapter Box 10.1). There is low confidence in possible effects of Arctic warming
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1 on mid-latitude temperature extremes (Cross-Chapter Box 10.1). A large portion of the multi-decadal
2 changes in extreme temperature remains after the removal of the effect of these modes of variability and can
3 be attributed to human influence (Kamae et al., 2017b; Wan et al., 2019). Thus, global warming dominates
4 changes in temperature extremes at the regional scale and it is very unlikely that dynamic responses to
5 greenhouse-gas induced warming would alter the direction of these changes.
6
7 Land-atmosphere feedbacks strongly modulate regional- and local-scale changes in temperature extremes
8 (high confidence; Section 11.1.6; Seneviratne et al., 2013; Lemordant et al., 2016; Donat et al., 2017;
9 Sillmann et al., 2017b; Hirsch et al., 2019). This effect is particularly notable in mid-latitude regions where
10 the drying of soil moisture amplifies high temperatures, in particular through increases in sensible heat flux
11 (Whan et al., 2015; Douville et al., 2016; Vogel et al., 2017). Land-atmosphere feedbacks amplifying
12 temperature extremes also include boundary-layer feedbacks and effects on atmospheric circulation
13 (Miralles et al., 2014a; Schumacher et al., 2019). Soil moisture-temperature feedbacks affect past and
14 present-day heat waves in observations and model simulations, both locally (Miralles et al. 2014; Hauser et
15 al. 2016; Meehl et al. 2016; Wehrli et al., 2019; Cowan et al., 2016) and beyond the regions of feedback
16 occurrence through changes in regional circulation patterns (Koster et al., 2016; Sato and Nakamura, 2019;
17 Stéfanon et al., 2014). The uncertainty due to the representation of land-atmosphere feedbacks in ESMs is a
18 cause of discrepancy between observations and simulations (Clark et al., 2006; Mueller and Seneviratne,
19 2014; Meehl et al., 2016). The decrease of plant transpiration or the increase of stomata resistance under
20 enhanced CO2 concentrations is a direct CO2 forcing of land temperatures (warming due to reduced
21 evaporative cooling), which contributes to higher warming on land (Lemordant et al., 2016; Vicente-Serrano
22 et al., 2020c). The snow/ice-albedo feedback plays an important role in amplifying temperature variability in
23 the high latitudes (Diro et al. 2018) and can be the largest contributor to the rapid warming of cold extremes
24 in the mid- and high latitudes of the Northern Hemisphere (Gross et al., 2020).
25
26 Regional external forcings, including land-use changes and emissions of anthropogenic aerosols, play an
27 important role in the changes of temperature extremes in some regions (high confidence, Section 11.1.6).
28 Deforestation may have contributed to about one third of the warming of hot extremes in some mid-latitude
29 regions since the pre-industrial time (Lejeune et al., 2018). Aspects of agricultural practice, including no-till
30 farming, irrigation, and overall cropland intensification, may cool hot temperature extremes (Davin et al.,
31 2014; Mueller et al., 2016b). For instance, cropland intensification has been suggested to be responsible for a
32 cooling of the highest temperature percentiles in the US Midwest (Mueller et al., 2016b). Irrigation has been
33 shown to be responsible for a cooling of hot temperature extremes of up to 1-2°C in many mid-latitude
34 regions in the present climate (Thiery et al., 2017; Thiery et al., 2020), a process not represented in most of
35 state-of-the-art ESMs (CMIP5, CMIP6). Double cropping may have led to increased hot extremes in the
36 inter-cropping season in part of China (Jeong et al., 2014). Rapid increases in summertime warming in
37 western Europe and northeast Asia since the 1980s are linked to a reduction in anthropogenic aerosol
38 precursor emissions over Europe (Dong et al., 2016, 2017; Nabat et al., 2014), in addition to the effect of
39 increased greenhouse gas forcing (see also Chapter 10, Section 10.1.3.1). This effect of aerosols on
40 temperature-related extremes is also noted for declines in short-lived anthropogenic aerosol emissions over
41 North America (Mascioli et al., 2016). On the local scale, the urban heat island (UHI) effect results in higher
42 temperatures in urban areas than in their surrounding regions and contributes to warming in regions of rapid
43 urbanization, in particular for night-time temperature extremes (Box 10.3; Phelan et al., 2015; Chapman et
44 al., 2017; Sun et al., 2019). But these local and regional forcings are generally not (well-) represented in the
45 CMIP5 and CMIP6 simulations (see also Section 11.3.3), contributing to uncertainty in model simulated
46 changes.
47
48 In summary, greenhouse gas forcing is the dominant driver leading to the warming of temperature extremes.
49 At regional scales, changes in temperature extremes are modulated by changes in large-scale patterns and
50 modes of variability, feedbacks including soil moisture-evapotranspiration-temperature or snow/ice-albedo-
51 temperature feedbacks, and local and regional forcings such as land use and land cover changes, or aerosol
52 concentrations, and decadal and multidecadal natural variability. This leads to heterogeneity in regional
53 changes and their associated uncertainties (high confidence). Urbanization has exacerbated the effects of
54 global warming in cities, in particular for night-time temperature extremes (high confidence).
55
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2 11.3.2 Observed trends
3
4 The SREX (IPCC, 2012) reported a very likely decrease in the number of cold days and nights and increase
5 in the number of warm days and nights at the global scale. Confidence in trends was assessed as regionally
6 variable (low to medium confidence) due to either a lack of observations or varying signals in sub-regions.
7
8 Since SREX (IPCC, 2012) and AR5 (IPCC, 2014), many regional-scale studies have examined trends in
9 temperature extremes using different metrics that are based on daily temperatures, such as the
10 CCl/WCRP/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) indices (Dunn et
11 al., 2020). The additional observational records, along with a stronger warming signal, show very clearly that
12 changes observed at the time of AR5 (IPCC, 2014) continued, providing strengthened evidence of an
13 increase in the intensity and frequency of hot extremes and decrease in the intensity and frequency of cold
14 extremes. While the magnitude of the observed trends in temperature-related extremes varies depending on
15 the region, spatial and temporal scales, and metric assessed, evidence of a warming effect is overwhelming,
16 robust, and consistent. In particular, an increase in the intensity and frequency of hot extremes is almost
17 always associated with an increase in the hottest temperatures and in the number of heatwave days. It is also
18 the case for changes in cold extremes. For this reason, and to simplify the presentation, the phrase “increase
19 in the intensity and frequency of hot extremes” is used to represent, collectively, an increase in the
20 magnitude of extreme day and/or night temperatures, in the number of warm days and/or nights, and in the
21 number of heat wave days. Changes in cold extremes are assessed similarly.
22
23 On the global scale, evidence of an increase in the number of warm days and nights and a decrease in the
24 number of cold days and nights, and an increase in the coldest and hottest extreme temperatures is very
25 robust and consistent among all variables. Figure 11.2 displays timeseries of globally-averaged annual
26 maximum daily maximum (TXx) and annual minimum daily minimum temperature (TNn) on land. Warming
27 of land mean TXx is similar to the mean land warming, which is about 45% higher than global warming
28 (Section 2.3.1). Warming of land mean TNn is even higher, with about 3°C of warming since 1960 (Figure
29 11.2). Figure 11.9 shows maps of linear trends over 1960-2018 in the annual maximum daily maximum
30 (TXx), the annual minimum daily minimum temperature (TNn), and frequency of warm days (TX90p). The
31 maps for TXx and TNn show trends consistent with overall warming in most regions, with a particularly
32 high warming of TXx in Europe and north-western South America, and a particularly high warming of TNn
33 in the Arctic. Consistent with the observed warming in global surface temperature (2.3.1.2) and the observed
34 trends in TXx and TNn, the frequency of TX90p has increased while that of cold nights (TN10p) has
35 decreased since the 1950s: Nearly all land regions showed statistically significant decreases in TN10p
36 (Alexander, 2016; Dunn et al., 2020), though trends in TX90p are variable with some decreases in southern
37 South America, mainly during austral summer (Rusticucci et al., 2017). A decrease in the number of cold
38 spell days is also observed over nearly all land surface areas (Easterling et al., 2016) and in the northern mid-
39 latitudes in particular (van Oldenborgh et al., 2019). These observed changes are also consistent when a new
40 global land surface daily air temperature dataset is analyzed (Zhang et al., 2019c). Consistent warming trends
41 in temperature extremes globally, and in most land areas, over the past century are also found in a range of
42 observation-based data sets (Fischer and Knutti, 2014; Donat et al., 2016a; Dunn et al., 2020), with the
43 extremes related to daily minimum temperatures changing faster than those related to daily maximum
44 temperatures (Dunn et al., 2020) (Fig. 11.2). Seasonal variations in trends in temperature-related extremes
45 have been demonstrated. A warming in warm-season temperature extremes is detected, even during the
46 “slower surface global warming” period from the late 1990s to early 2010s (Cross-Chapter Box 3.1) (Kamae
47 et al., 2014; Seneviratne et al., 2014; Imada et al., 2017). Many studies of past changes in temperature
48 extremes for particular regions or countries show trends consistent with this global picture, as summarized
49 below and in Tables 11.4, 11.7, 11.10, 11.13, 11.16 and 11.19 in Section 11.9.
50
51
52 [START FIGURE 11.9 HERE]
53
54 Figure 11.9: Linear trends over 1960-2018 in the annual maximum daily maximum temperature (TXx, a), the annual
55 minimum daily minimum temperature (TNn, b), and the annual number of days when daily maximum
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1 temperature exceeds its 90th percentile from a base period of 1961-1990 (TX90p, c), based on the
2 HadEX3 data set (Dunn et al., 2020). Linear trends are calculated only for grid points with at least 66% of
3 the annual values over the period and which extend to at least 2009. Areas without sufficient data are
4 shown in grey. No overlay indicates regions where the trends are significant at p = 0.1 level. Crosses
5 indicate regions where trends are not significant. For details on the methods see Supplementary Material
6 11.SM.2. Further details on data sources and processing are available in the chapter data table (Table
7 11.SM.9).
8
9 [END FIGURE 11.9 HERE]
10
11
12 In Africa (Table 11.4), while it is difficult to assess changes in temperature extremes in parts of the continent
13 because of a lack of data, evidence of an increase in the intensity and frequency of hot extremes and decrease
14 in the intensity and frequency of cold extremes is clear and robust in regions where data are available. These
15 include an increase in the frequency of warm days and nights and a decrease in the frequency of cold days
16 and nights with high confidence (Donat et al., 2013b, 2014b; Kruger and Sekele, 2013; Chaney et al., 2014;
17 Filahi et al., 2016; Moron et al., 2016; Ringard et al., 2016; Barry et al., 2018; Gebrechorkos et al., 2018) and
18 an increase in heat waves (Russo et al., 2016; Ceccherini et al., 2017). The increase in TNn is more notable
19 than in TXx (Figure 11.9). Cold spells occasionally strike subtropical areas, but are likely to have decreased
20 in frequency (Barry et al., 2018). The frequency of cold events has likely decreased in South Africa (Song et
21 al., 2014; Kruger and Nxumalo, 2017), North Africa (Driouech et al., 2021; Filahi et al., 2016), and the
22 Sahara (Donat et al., 2016a). Over the whole continent, there is medium confidence in an increase in the
23 intensity and frequency of hot extremes and decrease in the intensity and frequency of cold extremes; it is
24 likely that similar changes have also occurred in areas with poor data coverage, as warming is widespread
25 and as projected future changes are similar over all regions (11.3.5).
26
27 In Asia (Table 11.7), there is very robust evidence for a very likely increase in the intensity and frequency of
28 hot extremes and decrease in the intensity and frequency of cold extremes in recent decades. This is clear in
29 global studies (e.g. Alexander, 2016; Dunn et al., 2020), as well as in numerous regional studies (Table
30 11.7). The area fraction with extreme warmth in Asia increased during 1951–2016 (Imada et al., 2018). The
31 frequency of warm extremes increased and the frequency of cold extremes decreased in East Asia (Zhou et
32 al., 2016a; Chen and Zhai, 2017; Yin et al., 2017; Lee et al., 2018c; Qian et al., 2019) and west Asia (Acar
33 Deniz and Gönençgil, 2015; Erlat and Türkeş, 2016; Imada et al., 2017; Rahimi et al., 2018; Rahimi and
34 Hejabi, 2018) with high confidence. The duration of heat extremes has also lengthened in some regions, for
35 example, in southern China (Luo and Lau, 2016), but there is medium confidence of heat extremes increasing
36 in frequency in South Asia (AlSarmi and Washington, 2014; Sheikh et al., 2015; Mazdiyasni et al., 2017;
37 Zahid et al., 2017; Nasim et al., 2018; Khan et al., 2019; Roy, 2019). Warming trends in daily temperature
38 extremes indices have also been observed in central Asia (Hu et al., 2016; Feng et al., 2018), the Hindu Kush
39 Himalaya (Sun et al., 2017), and Southeast Asia (Supari et al., 2017; Cheong et al., 2018). The intensity and
40 frequency of cold spells in all Asian regions have been decreasing since the beginning of the 20th century
41 (high confidence) (Sheikh et al., 2015; Donat et al., 2016a; Dong et al., 2018; van Oldenborgh et al., 2019).
42
43 In Australasia (Table 11.10), there is very robust evidence for very likely increases in the number of warm
44 days and warm nights and decrease in the number of cold days and cold nights since 1950 (Lewis and King,
45 2015; Jakob and Walland, 2016; Alexander and Arblaster, 2017). The increase in extreme minimum
46 temperatures occurs in all seasons over most of Australia and typically exceeds the increase in extreme
47 maximum temperatures (Wang et al., 2013b; Jakob and Walland, 2016). However, some parts of southern
48 Australia have shown stable or increased numbers of frost days since the 1980s (Dittus et al., 2014) (see also
49 Section 11.3.4). Similar positive trends in extreme minimum and maximum temperatures have been
50 observed in New Zealand, in particular in the autumn-winter seasons, although they generally show higher
51 spatial variability (Caloiero, 2017). In the tropical Western Pacific region, spatially coherent warming trends
52 in maximum and minimum temperature extremes have been reported for the period of 1951–2011 (Whan et
53 al., 2014; McGree et al., 2019).
54
55 In Central and South America (Table 11.13), there is high confidence that observed hot extremes (TN90p,
56 TX90p) have increased and cold extremes (TN10p, TX10p) have decreased over recent decades, though
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1 trends vary among different extremes types, datasets, and regions (Dereczynski et al., 2020; Dittus et al.,
2 2016; Dunn et al., 2020; Meseguer-Ruiz et al., 2018; Olmo et al., 2020; Rusticucci et al., 2017; Salvador and
3 de Brito, 2018; Skansi et al., 2013). An increase in the intensity and frequency of heatwave events was also
4 observed between 1961 and 2014, in an area covering most of South America (Ceccherini et al., 2016;
5 Geirinhas et al., 2018). However, there is medium confidence that warm extremes (TXx and TX90p) have
6 decreased in the last decades over the central region of SES during austral summer (Tencer, B.; Rusticucci,
7 2012; Skansi et al., 2013; Rusticucci et al., 2017; Wu and Polvani, 2017). There is medium confidence that
8 TNn extremes are increasing faster than TXx extremes, with the largest warming rates observed over
9 Northeast Brazil (NEB) and North South America (NSA) for cold nights (Skansi et al., 2013).
10
11 In Europe (Table 11.16), there is very robust evidence for a very likely increase in maximum temperatures
12 and the frequency of heat waves. The increase in the magnitude and frequency of high maximum
13 temperatures has been observed consistently across regions including in central (Twardosz and Kossowska-
14 Cezak, 2013; Christidis et al., 2015; Lorenz et al., 2019) and southern Europe (Croitoru and Piticar, 2013; El
15 Kenawy et al., 2013; Christidis et al., 2015; Nastos and Kapsomenakis, 2015; Fioravanti et al., 2016; Ruml et
16 al., 2017). In northern Europe, a strong increase in extreme winter warming events has been observed
17 (Matthes et al., 2015; Vikhamar-Schuler et al., 2016). Temperature observations for wintertime cold spells
18 show a long-term decreasing frequency in Europe (Brunner et al., 2018; van Oldenborgh et al., 2019), and
19 typical cold spells such as that observed during the 2009/2010 winter had an occurrence probability that is
20 twice smaller currently than if climate change had not occurred (Christiansen et al., 2018).
21
22 In North America (Table 11.19), there is very robust evidence for a very likely increase in the intensity and
23 frequency of hot extremes and decrease in the intensity and frequency of cold extremes for the whole
24 continent, though there are substantial spatial and seasonal variations in the trends. Minimum temperatures
25 display warming consistently across the continent, while there are more contrasting trends in the annual
26 maximum daily temperatures in parts of the USA (Figure 11.9) (Lee et al., 2014; van Oldenborgh et al.,
27 2019; Dunn et al., 2020). In Canada, there is a clear increase in the intensity and frequency of hot extremes
28 and decrease in the intensity and frequency of cold extremes (Vincent et al., 2018). In Mexico, a clear
29 warming trend in TNn was found, particularly in the northern arid region (Montero-Martínez et al., 2018).
30 The number of warm days has increased and the number of cold days has decreased (García-Cueto et al.,
31 2019). Cold spells have undergone a reduction in magnitude and intensity in all regions of North America
32 (Bennett and Walsh, 2015; Donat et al., 2016a; Grotjahn et al., 2016; Vose et al., 2017; García-Cueto et al.,
33 2019; van Oldenborgh et al., 2019).
34
35 Extreme heat events have increased around the Arctic since 1979, particularly over Arctic North America
36 and Greenland (Matthes et al., 2015; Dobricic et al., 2020), which is consistent with summer melt (9.4.1).
37 Observations north of 60˚N show increases in wintertime warm days and nights over 1979-2015, while cold
38 days and nights declined (Sui et al., 2017). Extreme heat days are particularly strong in winter, with
39 observations showing the warmest mid-winter temperatures at the North Pole rising at twice the rate of mean
40 temperature (Moore, 2016), as well as increases in Arctic winter warm days (T>-10℃) (Vikhamar-Schuler et
41 al., 2016; Graham et al., 2017). Arctic annual minimum temperatures have increased at about three times the
42 rate of global surface temperature since the 1960s (Figs. 11.2, 11.9), consistent with the observed mean cold
43 season (October-May) warming of 3.1°C in the region (Atlas 11.2).
44
45 Trends in some measures of heat waves are also observed at the global scale. Globally-averaged heat wave
46 intensity, heat wave duration, and the number of heat wave days have significantly increased from 1950-
47 2011 (Perkins, 2015). There are some regional differences in trends in characteristics of heat waves with
48 significant increases reported in Europe (Russo et al., 2015; Forzieri et al., 2016; Sánchez-Benítez et al.,
49 2020) and Australia (CSIRO and BOM, 2016; Alexander and Arblaster, 2017). In Africa, there is medium
50 confidence that heat waves, regardless of the definition, have been becoming more frequent, longer-lasting,
51 and hotter over more than three decades (Fontaine et al., 2013; Mouhamed et al., 2013; Ceccherini et al.,
52 2016, 2017; Forzieri et al., 2016; Moron et al., 2016; Russo et al., 2016). The majority of heat wave
53 characteristics examined in China between 1961-2014 show increases in heat wave days, consistent with
54 warming (You et al., 2017; Xie et al., 2020). Increases in the frequency and duration of heat waves are also
55 observed in Mongolia (Erdenebat and Sato, 2016) and India (Ratnam et al., 2016; Rohini et al., 2016). In the
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1 UK, the lengths of short heat waves have increased since the 1970s, while the lengths of long heat waves
2 (over 10 days) have decreased over some stations in the southeast of England (Sanderson et al., 2017b). In
3 Central and South America, there are increases in the frequency of heat waves (Barros et al., 2015;
4 Bitencourt et al., 2016; Ceccherini et al., 2016; Piticar, 2018), although decreases in Excess Heat Factor
5 (EHF), which is a metric for heat wave intensity, are observed in South America in data derived from
6 HadGHCND (Cavanaugh and Shen, 2015).
7
8 In summary, it is virtually certain that there has been an increase in the number of warm days and nights and
9 a decrease in the number of cold days and nights on the global scale since 1950. Both the coldest extremes
10 and hottest extremes display increasing temperatures. It is very likely that these changes have also occurred
11 at the regional scale in Europe, Australasia, Asia, and North America. It is virtually certain that there has
12 been increases in the intensity and duration of heat waves and in the number of heat wave days at the global
13 scale. These trends likely occur in Europe, Asia, and Australia. There is medium confidence in similar
14 changes in temperature extremes in Africa and high confidence in South America; the lower confidence is
15 due to reduced data availability and fewer studies. Annual minimum temperatures on land have increased
16 about three times more than global surface temperature since the 1960s, with particularly strong warming in
17 the Arctic (high confidence).
18
19
20 11.3.3 Model evaluation
21
22 AR5 assessed that CMIP3 and CMIP5 models generally captured the observed spatial distributions of the
23 mean state and that the inter-model range of simulated temperature extremes was similar to the spread
24 estimated from different observational datasets; the models generally captured trends in the second half of
25 the 20th century for indices of extreme temperature, although they tended to overestimate trends in hot
26 extremes and underestimate trends in cold extremes (Flato et al., 2013). Post-AR5 studies on the CMIP5
27 models’ performance in simulating mean and changes in temperature extremes continue to support the AR5
28 assessment (Fischer and Knutti, 2014; Sillmann et al., 2014; Ringard et al., 2016; Borodina et al., 2017b;
29 Donat et al., 2017; Di Luca et al., 2020a). Over Africa, the observed warming in temperature extremes is
30 captured by CMIP5 models, although it is underestimated in west and central Africa (Sherwood et al., 2014;
31 Diedhiou et al., 2018). Over East Asia, the CMIP5 ensemble performs well in reproducing the observed
32 trend in temperature extremes averaged over China (Dong et al., 2015). Over Australia, the multi-model
33 mean performs better than individual models in capturing observed trends in gridded station based ETCCDI
34 temperature indices (Alexander and Arblaster, 2017).
35
36 Initial analyses of CMIP6 simulations (Chen et al., 2020a; Di Luca et al., 2020b; Kim et al., 2020; Li et al.,
37 2020a; Thorarinsdottir et al., 2020; Wehner et al., 2020) indicate the CMIP6 models perform similarly to the
38 CMIP5 models regarding biases in hot and cold extremes. In general, CMIP5 and CMIP6 historical
39 simulations are similar in their performance in simulating the observed climatology of extreme temperatures
40 (high confidence). The general warm bias in hot extremes and cold bias in cold extremes reported for CMIP5
41 models (Kharin et al., 2013; Sillmann et al., 2013a) remain in CMIP6 models (Di Luca et al., 2020b).
42 However, there is some evidence that CMIP6 models better represent some of the underlying processes
43 leading to extreme temperatures, such as seasonal and diurnal variability and synoptic-scale variability (Di
44 Luca et al., 2020b). Whether these improvements are sufficient to enhance our understanding of past changes
45 or to reduce uncertainties in future projections remains unclear. The relative error estimates in the simulation
46 of various indices of temperature extremes in the available CMIP6 models show that no single model
47 performs the best on all indices and the multi-model ensemble seems to out-perform any individual model
48 due to its reduction in systematic bias (Kim et al., 2020). Figure 11.10 show errors in the 1979-2014 average
49 annual TXx and annual TNn simulated by available CMIP6 models in comparison with HadEX3 and ERA5
50 (Li et al., 2020; Kim et al., 2020, Wehner et al., 2020). While the magnitude of the model error depends on
51 the reference data set, the model evaluations drawn from different reference data sets are quite similar. In
52 general, models reproduce the spatial patterns and magnitudes of both cold and hot temperature extremes
53 quite well. There are also systematic biases. Hot extremes tend to be too cool in mountainous and high-
54 latitude regions, but too warm in the eastern United States and South America. For cold extremes, CMIP6
55 models are too cool, except in northeastern Eurasia and the southern mid-latitudes. Errors in seasonal mean
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1 temperatures are uncorrelated with errors in extreme temperatures and are often of opposite sign (Wehner et
2 al., 2020).
3
4
5 [START FIGURE 11.10 HERE]
6
7 Figure 11.10:Multi-model mean bias in temperature extremes (°C ) for the period 1979-2014, calculated as the
8 difference between the CMIP6 multi-model mean and the average of observations from the values
9 available in HadEX3 for (a) the annual hottest temperature (TXx) and (b) the annual coldest temperature
10 (TNn). Areas without sufficient data are shown in grey. Adapted from Wehner et al. (2020) under the
11 terms of the Creative Commons Attribution license. Further details on data sources and processing are
12 available in the chapter data table (Table 11.SM.9).
13
14 [END FIGURE 11.10 HERE]
15
16
17 Atmospheric model (AMIP) simulations are often used in event attribution studies to assess the influence of
18 global warming on observed temperature-related extremes. These simulations typically capture the observed
19 trends in temperature extremes, though some regional features, such as the lack of warming in daytime warm
20 temperature extremes over South America and parts of North America, are not reproduced in the model
21 simulations (Dittus et al., 2018), possibly due to internal variability, deficiencies in local surface processes,
22 or forcings that are not represented in the SSTs. Additionally, the AMIP models assessed tend to produce
23 overly persistent heat wave events. This bias in the duration of the events does not impact the reliability of
24 the models’ positive trends (Freychet et al., 2018).
25
26 Several regional climate models (RCMs) have also been evaluated in terms of their performance in
27 simulating the climatology of extremes in various regions of the Coordinated Regional Downscaling
28 Experiment (CORDEX) (Giorgi et al., 2009), especially in East Asia (Ji and Kang, 2015; Yu et al., 2015;
29 Park et al., 2016; Bucchignani et al., 2017; Gao et al., 2017a; Niu et al., 2018; Sun et al., 2018b; Wang et al.,
30 2018a), Europe (Cardoso et al., 2019; Gaertner et al., 2018; Jacob et al., 2020; Kim et al., 2020; Lorenz et
31 al., 2019; Smiatek et al., 2016; Vautard et al., 2013; Vautard et al., 2020b), and Africa (Kim et al., 2014b;
32 Diallo et al., 2015; Dosio, 2017; Samouly et al., 2018; Mostafa et al., 2019). Compared to GCMs, RCM
33 simulations show an added value in simulating temperature-related extremes, though this depends on
34 topographical complexity and the parameters employed (see Section 10.3.3). The improvement with
35 resolution is noted in East Asia (Park et al., 2016; Zhou et al., 2016b; Shi et al., 2017; Hui et al., 2018).
36 However, in the European CORDEX ensemble, different aerosol climatologies with various degrees of
37 complexity were used in projections (Bartók et al., 2017; Lorenz et al., 2019) and the land surface models
38 used in the RCMs do not account for physiological CO2 effects on photosynthesis leading to enhanced water-
39 use efficiency and decreased evapotranspiration (Schwingshackl et al., 2019), which could lead to biases in
40 the representation of temperature extremes in these projections (Boé et al., 2020). In addition, there are key
41 cold biases in temperature extremes over areas with complex topography (Niu et al., 2018). Over North
42 America, 12 RCMs were evaluated over the ARCTIC-CORDEX region (Diaconescu et al., 2018). Models
43 were able to simulate well climate indices related to mean air temperature and hot extremes over most of the
44 Canadian Arctic, with the exception of the Yukon region where models displayed the largest biases related to
45 topographic effects. Two RCMs were evaluated against observed extremes indices over North America over
46 the period 1989–2009, with a cool bias in minimum temperature extremes shown in both RCMs (Whan and
47 Zwiers, 2016). The most significant biases are found in TXx and TNn, with fewer differences in the
48 simulation of annual minimum daily maximum temperature (TXn) and annual maximum daily minimum
49 temperature (TNx) in central and western North America. Over Central and South America, maximum
50 temperatures from the Eta RCM are generally underestimated, although hot days, warm nights, and heat
51 waves are increasing in the period 1961-1990, in agreement with observations (Chou et al., 2014b; Tencer et
52 al., 2016; Bozkurt et al., 2019).
53
54 Some land forcings are not well represented in climate models. As highlighted in the IPCC SRCCL Ch2,
55 there is high agreement that temperate deforestation leads to summer warming and winter cooling (Bright et
56 al., 2017; Zhao and Jackson, 2014; Gálos et al., 2011, 2013; Wickham et al., 2013; Ahlswede and Thomas,
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1 2017; Anderson-Teixeira et al., 2012; Anderson et al., 2011; Chen et al., 2012; Strandberg and Kjellström,
2 2019), which has substantially contributed to the warming of hot extremes in the northern mid-latitudes over
3 the course of the 20th century (Lejeune et al., 2018) and in recent years (Strandberg and Kjellström, 2019).
4 However, observed forest effects on the seasonal and diurnal cycle of temperature are not well captured in
5 several ESMs: while observations show a cooling effect of forest cover compared to non-forest vegetation
6 during daytime (Li et al., 2015), in particular in arid, temperate, and tropical regions (Alkama and Cescatti,
7 2016), several ESMs simulate a warming of daytime temperatures for regions with forest vs non-forest cover
8 (Lejeune et al., 2017). Also irrigation effects, which can lead to regional cooling of temperature extremes,
9 are generally not integrated in current-generations of ESMs (Section 11.3.1).
10
11 In summary, there is high confidence that climate models can reproduce the mean state and overall warming
12 of temperature extremes observed globally and in most regions, although the magnitude of the trends may
13 differ. The ability of models to capture observed trends in temperature-related extremes depends on the
14 metric evaluated, the way indices are calculated, and the time periods and spatial scales considered. Regional
15 climate models add value in simulating temperature-related extremes over GCMs in some regions. Some
16 land forcings on temperature extremes are not well captured (effects of deforestation) or generally not
17 representated (irrigation) in ESMs.
18
19
20 11.3.4 Detection and attribution, event attribution
21
22 SREX (IPCC, 2012) assessed that it is likely anthropogenic influences have led to the warming of extreme
23 daily minimum and maximum temperatures at the global scale. AR5 concluded that human influence has
24 very likely contributed to the observed changes in the intensity and frequency of daily temperature extremes
25 on the global scale in the second half of the 20th century (IPCC, 2014). With regard to individual, or
26 regionally- or locally-specific events, AR5 concluded that it is likely human influence has substantially
27 increased the probability of occurrence of heat waves in some locations.
28
29 Studies since AR5 continue to attribute the observed increase in the frequency or intensity of hot extremes
30 and the observed decrease in the frequency or intensity of cold extremes to human influence, dominated by
31 anthropogenic greenhouse gas emissions, on global and continental scales, and for many AR6 regions. These
32 include attribution of changes in the magnitude of annual TXx, TNx, TXn, and TNn, based on different
33 observational data sets including, HadEX2 and HadEX3, CMIP5 and CMIP6 simulations, and different
34 statistical methods (Kim et al., 2016; Wang et al., 2017c; Seong et al., 2020). As is the case for an increase in
35 mean temperature (3.3.1), an increase in extreme temperature is mostly due to greenhouse gas forcing, off-
36 set by aerosol forcing. The aerosols’ cooling effect is clearly detectable over Europe and Asia (Seong et al.,
37 2020). As much as 75% of the moderate daily hot extremes (above 99.9th percentile) over land are due to
38 anthropogenic warming (Fischer and Knutti, 2015). New results are found to be more robust due to the
39 extended period that improves the signal-to-noise ratio. The effect of anthropogenic forcing is clearly
40 detectable and attributable in the observed changes in these indicators of temperature extremes, even at
41 country and sub-country scales, such as in Canada (Wan et al., 2019). Changes in the number of warm
42 nights, warm days, cold nights, and cold days, and other indicators such as the Warm Spell Duration Index
43 (WSDI), are also attributed to anthropogenic influence (Hu et al., 2020; Christidis and Stott, 2016).
44
45 Regional studies, including for Asia (Dong et al., 2018; Lu et al., 2018), Australia (Alexander and Arblaster,
46 2017), and Europe (Christidis and Stott, 2016), found similar results. A clear anthropogenic signal is also
47 found in the trends in the Combined Extreme Index (CEI) for North America, Asia, Australia, and Europe
48 (Dittus et al., 2016). While various studies have described increasing trends in several heat wave metrics
49 (HWD, HWA, EHF, etc.) in different regions (e.g., Bandyopadhyay et al., 2016; Cowan et al., 2014;
50 Sanderson et al., 2017), few recent studies have explicitly attributed these changes to causes; most of them
51 stated that observed trends are consistent with anthropogenic warming. The detected anthropogenic signals
52 are clearly separable from the response to natural forcing, and the results are generally insensitive to the use
53 of different model samples, as well as different data availability, indicating robust attribution. Studies of
54 monthly, seasonal, and annual records in various regions (Kendon, 2014; Lewis and King, 2015; Bador et al.,
55 2016; Meehl et al., 2016; Zhou et al., 2019a) and globally (King, 2017) show an increase in the breaking of
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1 hot records and a decrease in the breaking of cold records (King, 2017). Changes in anthropogenically-
2 attributable record-breaking rates are noted to be largest over the Northern Hemisphere land areas (Shiogama
3 et al., 2016). Yin and Sun (2018) found clear evidence of an anthropogenic signal in the changes in the
4 number of frost and icing days, when multiple model simulations were used. In some key wheat-producing
5 regions of southern Australia, increases in frost days or frost season length have been reported (Dittus et al.,
6 2014; Crimp et al., 2016); these changes are linked to decreases in rainfall, cloud-cover, and subtropical
7 ridge strength, despite an overall increase in regional mean temperatures (Dittus et al., 2014; Pepler et al.,
8 2018).
9
10 A significant advance since AR5 has been a large number of studies focusing on extreme temperature events
11 at monthly and seasonal scales, using various extreme event attribution methods. Diffenbaugh et al. (2017)
12 found anthropogenic warming has increased the severity and probability of the hottest month over >80% of
13 the available observational area on the global scale. Christidis and Stott (2014) provide clear evidence that
14 warm events have become more probable because of anthropogenic forcings. Sun et al. (2014) found human
15 influence has caused a more than 60-fold increase in the probability of the extreme warm 2013 summer in
16 eastern China since the 1950s. Human influence is found to have increased the probability of the historically
17 hottest summers in many regions of the world, both in terms of mean temperature (Mueller et al., 2016a) and
18 wet-bulb globe temperature (WBGT) (Li et al., 2017a). In most regions of the Northern Hemisphere,
19 changes in the probability of extreme summer average WBGT were found to be about an order of magnitude
20 larger than changes in the probability of extreme hot summers estimated by surface air temperature (Li et al.,
21 2017a). In addition to these generalised, global-scale approaches, extreme event studies have found an
22 attributable increase in the probability of hot annual and seasonal temperatures in many locations, including
23 Australia (Knutson et al., 2014a; Lewis and Karoly, 2014), China (Sun et al., 2014; Sparrow et al., 2018;
24 Zhou et al., 2020), Korea (Kim et al., 2018c) and Europe (King et al., 2015b).
25
26 There have also been many extreme event attribution studies that examined short duration temperature
27 extremes, including daily temperatures, temperature indices, and heat wave metrics. Examples of these
28 events from different regions are summarised in various annual Explaining Extreme Events supplements of
29 the Bulletin of the American Meteorological Society (Peterson et al., 2012, 2013b, Herring et al., 2014,
30 2015, 2016, 2018, 2019, 2020), including a number of approaches to examine extreme events (described in
31 Easterling et al., 2016; Otto, 2017; Stott et al., 2016). Several studies of recent events from 2016 onwards
32 have determined an infinite risk ratio (fraction of attributable risk (FAR) of 1), indicating the occurrence
33 probability for such events is close to zero in model simulations without anthropogenic influences (see
34 Herring et al., 2018, 2019, 2020; Imada et al., 2019; Vogel et al., 2019). Though it is difficult to accurately
35 estimate the lower bound of the uncertainty range of the FAR in these cases (Paciorek et al., 2018), the fact
36 that those events are so far outside the envelop of the models with only natural forcing indicates that it is
37 extremely unlikely for those events to occur without human influence.
38
39 Studies that focused on the attributable signal in observed cold extreme events show human influence
40 reducing the probability of those events. Individual attribution studies on the extremely cold winter of 2011
41 in Europe (Peterson et al., 2012), in the eastern US during 2014 and 2015 (Trenary et al., 2015, 2016; Wolter
42 et al., 2015; Bellprat et al., 2016), in the cold spring of 2013 in the United Kingdom (Christidis et al., 2014),
43 and of 2016 in eastern China (Qian et al., 2018; Sun et al., 2018b) all showed a reduced probability due to
44 human influence on the climate. An exception is the study of Grose et al. (2018), who found an increase in
45 the probability of the severe western Australian frost of 2016 due to anthropogenically-driven changes in
46 circulation patterns that drive cold outbreaks and frost probability.
47
48 Different event attribution studies can produce a wide range of changes in the probability of event
49 occurrence because of different framing. The temperature event definition itself plays a crucial role in the
50 attributable signal (Fischer and Knutti, 2015; Kirchmeier‐Young et al., 2019). Large-scale, longer-duration
51 events tend to have notably larger attributable risk ratios (Angélil et al., 2014, 2018; Uhe et al., 2016;
52 Harrington, 2017; Kirchmeier‐Young et al., 2019), as natural variability is smaller. While uncertainty in the
53 best estimates of the risk ratios may be large, their lower bounds can be quite insensitive to uncertainties in
54 observations or model descriptions, thus increasing confidence in conservative attribution statements (Jeon et
55 al., 2016).
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1
2 The relative strength of anthropogenic influences on temperature extremes is regionally variable, in part due
3 to differences in changes in atmospheric circulation, land surface feedbacks, and other external drivers like
4 aerosols. For example, in the Mediterranean and over western Europe, risk ratios on the order of 100 have
5 been found (Kew et al., 2019; Vautard et al., 2020a), whereas in the US, changes are much less pronounced.
6 This is probably a reflection of the land-surface feedback enhanced extreme 1930s temperatures that reduce
7 the rarity of recent extremes, in addition to the definition of the events and framing of attribution analyses
8 (e.g., spatial and temporal scales considered). Local forcing may mask or enhance the warming effect of
9 greenhouse gases. In India, short-lived aerosols or an increase in irrigation may be masking the warming
10 effect of greenhouse gases (Wehner et al., 2018c). Irrigation and crop intensification have been shown to
11 lead to a cooling in some regions, in particular in North America, Europe, and India (Mueller et al., 2016b;
12 Thiery et al., 2017, 2020; Chen and Dirmeyer, 2019),(high confidence). Deforestation has contributed about
13 one third of the total warming of hot extremes in some mid-latitude regions since pre-industrial times
14 (Lejeune et al., 2018). Despite all of these differences, and larger uncertainties at the regional scale, nearly
15 all studies demonstrated that human influence has contributed to an increase in the frequency or intensity of
16 hot extremes and to a decrease in the frequency or intensity of cold extremes.
17
18 In summary, long-term changes in various aspects of long- and short-duration extreme temperatures,
19 including intensity, frequency, and duration have been detected in observations and attributed to human
20 influence at global and continental scales. It is extremely likely that human influence is the main contributor
21 to the observed increase in the intensity and frequency of hot extremes and the observed decrease in the
22 intensity and frequency of cold extremes on the global scale. It is very likely that this applies on continental
23 scales as well. Some specific recent hot extreme events would have been extremely unlikely to occur without
24 human influence on the climate system. Changes in aerosol concentrations have affected trends in hot
25 extremes in some regions, with the presence of aerosols leading to attenuated warming, in particular from
26 1950-1980. Crop intensification, irrigation and no-till farming have attenuated increases in summer hot
27 extremes in some regions, such as central North America (medium confidence).
28
29
30 11.3.5 Projections
31
32 AR5 (Chapter12, Collins et al., 2013a) concluded it is virtually certain there will be more frequent hot
33 extremes and fewer cold extremes at the global scale and over most land areas in a future warmer climate
34 and it is very likely heat waves will occur with a higher frequency and longer duration . SR15 (Chapter 3,
35 Hoegh-Guldberg et al., 2018) assessment on projected changes in hot extremes at 1.5°C and 2°C global
36 warming is consistent with the AR5 assessment, concluding it is very likely a global warming of 2°C, when
37 compared with a 1.5°C warming, would lead to more frequent and more intense hot extremes on land, as
38 well as to longer warm spells, affecting many densely-inhabited regions. SR15 also assessed it is very likely
39 the strongest increases in the frequency of hot extremes are projected for the rarest events, while cold
40 extremes will become less intense and less frequent and cold spells will be shorter.
41
42 New studies since AR5 and SR15 confirm these assessments. New literature since AR5 includes projections
43 of temperature-related extremes in relation to changes in mean temperatures, projections based on CMIP6
44 simulations, projections based on stabilized global warming levels, and the use of new metrics. Constraints
45 for the projected changes in hot extremes were also provided (Borodina et al., 2017b; Sippel et al., 2017b;
46 Vogel et al., 2017). Overall, projected changes in the magnitude of extreme temperatures over land are larger
47 than changes in global mean temperature, over mid-latitude land regions in particular (Figures 11.3, 11.11),
48 (Fischer et al., 2014; Seneviratne et al., 2016; Sanderson et al., 2017a; Wehner et al., 2018b; Di Luca et al.,
49 2020a). Large warming in hot and cold extremes will occur even at the 1.5°C global warming level (Figure
50 11.11). At this level, widespread significant changes at the grid-box level occur for different temperature
51 indices (Aerenson et al., 2018). In agreement with CMIP5 projections, CMIP6 simulations show that a 0.5°C
52 increment in global warming will significantly increase the intensity and frequency of hot extremes and
53 decrease the intensity and frequency of cold extremes on the global scale (Figures 11.6, 11.8, 11.12). It takes
54 less than half of a degree for the changes in TXx to emerge above the level of natural variability (Figure
55 11.8) and the 66% ranges of the land medians of the 10-year or 50-year TXx events do not overlap between
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1 1.0°C and 1.5°C in the CMIP6 multi-model ensemble simulations (Figure 11.6, Li et al., 2020).
2
3
4 [START FIGURE 11.11 HERE]
5
6
7 Figure 11.11:Projected changes in (a-c) annual maximum temperature (TXx) and (d-f) annual minimum temperature
8 (TNn) at 1.5°C, 2°C, and 4°C of global warming compared to the 1851-1900 baseline. Results are based
9 on simulations from the CMIP6 multi-model ensemble under the SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-
10 7.0, and SSP5-8.5 scenarios. The numbers in the top right indicate the number of simulations included.
11 Uncertainty is represented using the simple approach: no overlay indicates regions with high model
12 agreement, where ≥80% of models agree on sign of change; diagonal lines indicate regions with low
13 model agreement, where <80% of models agree on sign of change. For more information on the simple
14 approach, please refer to the Cross-Chapter Box Atlas 1. For details on the methods see Supplementary
15 Material 11.SM.2. Further details on data sources and processing are available in the chapter data table
16 (Table 11.SM.9).
17
18 [END FIGURE 11.11 HERE]
19
20
21 Projected warming is larger for TNn and exhibits strong equator-to-pole amplification similar to the warming
22 of boreal winter mean temperatures. The warming of TXx is more uniform over land and does not exhibit
23 this behaviour (Figure 11.11). The warming of temperature extremes on global and regional scales tends to
24 scale linearly with global warming (Section 11.1.4) (Fischer et al., 2014; Seneviratne et al., 2016,
25 Wartenburger et al., 2017; Li et al., 2020; see also SR15, Chapter 3). In the mid-latitudes, the rate of
26 warming of hot extremes can be as large as twice the rate of global warming (Figure 11.11). In the Arctic
27 winter, the rate of warming of the temperature of the coldest nights is about three times the rate of global
28 warming (Appendix Figure 11.A.1). Projected changes in temperature extremes can deviate from projected
29 changes in annual mean warming in the same regions (Figure 11.3, Figs. 11.A.1 and 11.A.2, Di Luca et al.,
30 2020a; Wehner, 2020) due to the additional processes that control the response of regional extremes,
31 including, in particular, soil moisture-evapotranspiration-temperature feedbacks for hot extremes in the mid-
32 latitudes and subtropical regions, and snow/ice-albedo-temperature feedbacks in high-latitude regions.
33
34
35 [START FIGURE 11.12 HERE]
36
37 Figure 11.12:Projected changes in the intensity of extreme temperature events under 1°C, 1.5°C, 2°C, 3°C, and 4°C
38 global warming levels relative to the 1851-1900 baseline. Extreme temperature events are defined as the
39 daily maximum temperatures (TXx) that were exceeded on average once during a 10-year period (10-year
40 event, blue) and that once during a 50-year period (50-year event, orange) during the 1851-1900 base
41 period. Results are shown for the global land. For each box plot, the horizontal line and the box represent
42 the median and central 66% uncertainty range, respectively, of the intensity changes across the space, and
43 the whiskers extend to the 90% uncertainty range. The results are based on the multi-model ensemble
44 median estimated from simulations of global climate models contributing to the sixth phase of the
45 Coupled Model Intercomparison Project (CMIP6) under different SSP forcing scenarios. Adapted from
46 (Li et al., 2020a). Further details on data sources and processing are available in the chapter data table
47 (Table 11.SM.9).
48
49 [END FIGURE 11.12 HERE]
50
51
52 The probability of exceeding a certain hot extreme threshold will increase, while those for cold extreme will
53 decrease with global warming (Mueller et al., 2016a; Lewis et al., 2017b; Suarez-Gutierrez et al., 2020b).
54 The changes tend to scale nonlinearly with the level of global warming, with larger changes for more rare
55 events (Section 11.2.4 and CCB 11.11; Figure. 11.6 and 11.12; e.g. Fischer and Knutti, 2015, Kharin et al.,
56 2018; Li et al., 2020). For example, the CMIP5 ensemble projects the frequency of the present-day climate
57 20-year hottest daily temperature to increase by 80% at the 1.5°C global warming level and by 180% at the
58 2.0°C global warming level, and the frequency of the present-day climate 100-year hottest daily temperature
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1 to increase by 200% and more than 700% at the 1.5°C and 2.0°C warming levels, respectively (Kharin et al.,
2 2018). CMIP6 simulations project similar changes (Li et al., 2020a).
3
4 Tebaldi and Wehner (2018) showed that at the middle of the 21st century, 66% of the land surface area would
5 experience the present-day 20-year return values of TXx and the running 3-day average of the daily
6 maximum temperature every other year on average under the RCP8.5 scenario, as opposed to only 34%
7 under RCP4.5. By the end of the century, these area fractions increase to 92% and 62%, respectively. Such
8 nonlinearities in the characteristics of future regional extremes are shown, for instance, for Europe (Lionello
9 and Scarascia, 2020; Spinoni et al., 2018a; Dosio and Fischer, 2018), Asia (Guo et al., 2017; Harrington and
10 Otto, 2018b; King et al., 2018), and Australia (Lewis et al., 2017a) under various global warming thresholds.
11 The non-linear increase in fixed-threshold indices (e.g., percentile-based for a given reference period or
12 based on an absolute threshold) as a function of global warming is consistent with a linear warming of the
13 absolute temperature of the temperature extremes (e.g., Whan et al., 2015). Compared to the historical
14 climate, warming will result in strong increases in heat wave area, duration, and magnitude (Vogel et al.,
15 2020b). These changes are mostly due to the increase in mean seasonal temperature, rather than changes in
16 temperature variability, though the latter can have an effect in some regions (Di Luca et al., 2020a; Suarez-
17 Gutierrez et al., 2020a; Brown, 2020).
18
19 Projections of temperature-related extremes in RCMs in the CORDEX regions demonstrate robust increases
20 under future scenarios and can provide information on finer spatial scales than GCMs (e.g. Coppola et al.,
21 2021). Five RCMs in the CORDEX-East Asia region project decreases in the 20-year return values of
22 temperature extremes (summer maxima), with models that exhibit warm biases projecting stronger warming
23 (Park and Min, 2018). Similarly, in the African domain, future increases in TX90p and TN90p are projected
24 (Dosio, 2017; Mostafa et al., 2019). This regional-scale analysis provides fine scale information, such as
25 distinguishing the increase in TX90p over sub-equatorial Africa (Democratic Republic of Congo, Angola
26 and Zambia) with values over the Gulf of Guinea, Central African Republic, South Sudan, and Ethiopia.
27 Empirical-statistical downscaling has also been used to produce more robust estimates for future heat waves
28 compared to RCMs based on large multi-model ensembles (Furrer et al., 2010; Keellings and Waylen, 2014;
29 Wang et al., 2015; Benestad et al., 2018).
30
31 In all continental regions, including Africa (Table 11.4), Asia (Table 11.7), Australasia (Table 11.10),
32 Central and South America (Table 11.13), Europe (Table 11.16), North America (Table 11.19) and at the
33 continental scale, it is very likely the intensity and frequency of hot extremes will increase and the intensity
34 and frequency of cold extremes will decrease compared with the 1995-2014 baseline, even under 1.5°C
35 global warming, and those changes are virtually certain to occur under 4°C global warming. At the regional
36 scale and for almost all AR6 regions, it is likely the intensity and frequency of hot extremes will increase and
37 the intensity and frequency of cold extremes will decrease compared with the 1995-2014 baseline, even
38 under 1.5°C global warming and those changes will virtually certain to occur under 4°C global warming.
39 Exceptions include lower confidence in the projected decrease in the intensity and frequency of cold
40 extremes compared with the 1995-2014 baseline under 1.5°C of global warming (medium confidence) and
41 4°C of global warming (very likely) in North Central America, Central North America, and Western North
42 America.
43
44 In Africa (Table 11.4), evidence includes increases in the intensity and frequency of hot extremes, such as
45 warm days, warm nights, and heat waves, and decreases in the intensity and frequency of cold extremes,
46 such as cold days and cold nights, over the continent as projected by CMIP5, CMIP6, and CORDEX
47 simulations (Giorgi et al., 2014; Engelbrecht et al., 2015; Lelieveld et al., 2016; Russo et al., 2016; Dosio,
48 2017; Bathiany et al., 2018; Mba et al., 2018; Nangombe et al., 2018; Weber et al., 2018; Kruger et al., 2019;
49 Coppola et al., 2021; Li et al., 2020). Cold spells are projected to decrease under all RCPs and even at low
50 warming levels in West and Central Africa (Diedhiou et al., 2018) and the number of cold days is projected
51 to decrease in East Africa (Ongoma et al., 2018b).
52
53 In Asia (Table 11.7), evidence includes increases in the intensity and frequency of hot extremes, such as
54 warm days, warm nights, and heat waves, and decreases in the intensity and frequency of cold extremes,
55 such as cold days and cold nights, over the continent as projected by CMIP5, CMIP6, and CORDEX
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1 simulations (Gao et al., 2018; Han et al., 2018; Li et al., 2019b; Pal and Eltahir, 2016; Shin et al., 2018;
2 Sillmann et al., 2013b; Singh and Goyal, 2016; Sui et al., 2018; Xu et al., 2017; Zhang et al., 2015c; Zhao et
3 al., 2015; Zhou et al., 2014; Zhu et al., 2020). More intense heat waves of longer durations and occurring at a
4 higher frequency are projected over India (Murari et al., 2015; Mishra et al., 2017) and Pakistan (Nasim et
5 al., 2018). Future mid-latitude warm extremes, similar to those experienced during the 2010 event, are
6 projected to become more extreme, with temperature extremes increasing potentially by 8.4°C (RCP8.5)
7 over northwest Asia (van der Schrier et al., 2018). Over WSB, ESB and RFE, an increase in extreme heat
8 durations is expected in all scenarios (Sillmann et al., 2013b; Kattsov et al., 2017; Reyer et al., 2017). In the
9 MENA regions (ARP, WCA), extreme temperatures could increase by almost 7°C by 2100 under RCP8.5
10 (Lelieveld et al., 2016).
11
12 In Australasia (Table 11.10), evidence includes increases in the intensity and frequency of hot extremes, such
13 as warm days, warm nights, and heat waves, and decreases in the intensity and frequency of cold extremes,
14 such as cold days and cold nights, over the continent as projected by CMIP5, CMIP6, and CORDEX
15 simulations (Coppola et al., 2021; Alexander and Arblaster, 2017; CSIRO and BOM, 2015; Herold et al.,
16 2018; Lewis et al., 2017a; Evans et al., 2020). Over most of Australia, increases in the intensity and
17 frequency of hot extremes are projected to be predominantly driven by the long-term increase in mean
18 temperatures (Di Luca et al., 2020a). Future projections indicate a decrease in the number of frost days
19 regardless of the region and season considered (Alexander and Arblaster, 2017; Herold et al., 2018).
20
21 In Central and South America (Table 11.13), evidence includes increases in the intensity and frequency of
22 hot extremes, such as warm days, warm nights, and heat waves, and decreases in the intensity and frequency
23 of cold extremes, such as cold days and cold nights, over the continent as projected by CMIP5, CMIP6, and
24 CORDEX simulations (Chou et al., 2014a; Cabré et al., 2016; López-Franca et al., 2016; Stennett-Brown et
25 al., 2017; Li et al., 2020a; Coppola et al., 2021b; Vichot-Llano et al., 2021). Over SES during the austral
26 summer, the increase in the frequency of TN90p is larger than that projected for TX90p, consistent with
27 observed past changes (López-Franca et al., 2016). Under RCP8.5, the number of heat wave days are
28 projected to increase for the intra-Americas region for the end of the 21st century (Angeles-Malaspina et al.,
29 2018). A general decrease in the frequency of cold spells and frost days is projected as indicated by several
30 indices based on minimum temperature (López-Franca et al., 2016).
31
32 In Europe (Table 11.16), evidence includes increases in the intensity and frequency of hot extremes, such as
33 warm days, warm nights, and heat waves, and decreases in the intensity and frequency of cold extremes,
34 such as cold days and cold nights, over the continent as projected by CMIP5, CMIP6, and CORDEX
35 simulations (Coppola et al., 2021; Cardoso et al., 2019; Jacob et al., 2018; Lau and Nath, 2014; Lhotka et al.,
36 2018; Lionello and Scarascia, 2020; Molina et al., 2020; Ozturk et al., 2015; Rasmijn et al., 2018; Russo et
37 al., 2015; Schoetter et al., 2015; Suarez-Gutierrez et al., 2018; Vogel et al., 2017; Winter et al., 2017; Li et
38 al., 2020). Increases in heat waves are greater over the southern Mediterranean and Scandinavia (Forzieri et
39 al., 2016; Abaurrea et al., 2018; Dosio and Fischer, 2018; Rohat et al., 2019). The biggest increases in the
40 number of heat wave days are expected for southern European cities (Guerreiro et al., 2018a; Junk et al.,
41 2019), and Central European cities will see the biggest increases in maximum heat wave temperatures
42 (Guerreiro et al., 2018a).
43
44 In North America (Table 11.19), evidence includes increases in the intensity and frequency of hot extremes,
45 such as warm days, warm nights, and heat waves, and decreases in the intensity and frequency of cold
46 extremes, such as cold days and cold nights, over the continent as projected by CMIP5, CMIP6, and
47 CORDEX simulations (Li et al., 2020; Coppola et al., 2021; Alexandru, 2018; Grotjahn et al., 2016; Li et al.,
48 2018a; Vose et al., 2017a; Yang et al., 2018a; Zhang et al., 2019d). Projections of temperature extremes for
49 the end of the 21st century show that warm days and nights are very likely to increase and cold days and
50 nights are very likely to decrease in all regions. There is medium confidence in large increases in warm days
51 and warm nights in summer, particularly over the United States, and in large decreases in cold days in
52 Canada in fall and winter (Li et al., 2020; Coppola et al., 2021; Alexandru, 2018; Grotjahn et al., 2016; Li et
53 al., 2018a; Vose et al., 2017a; Yang et al., 2018a; Zhang et al., 2019d). Minimum winter temperatures are
54 projected to rise faster than mean winter temperatures (Underwood et al., 2017). Projections for the end of
55 the century under RCP8.5 showed the 4-day cold spell that happens on average once every 5 years is
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1 projected to warm by more than 10 ºC and CMIP5 models do not project current 1-in-20 year annual
2 minimum temperature extremes to recur over much of the continent (Wuebbles et al., 2014).
3
4 In summary, it is virtually certain that further increases in the intensity and frequency of hot extremes and
5 decreases in the intensity and frequency of cold extremes will occur throughout the 21st century and around
6 the world. It is virtually certain the number of hot days and hot nights and the length, frequency, and/or
7 intensity of warm spells or heat waves compared to 1995-2014 will increase over most land areas. In most
8 regions, changes in the magnitude of temperature extremes are proportional to global warming levels (high
9 confidence). The highest increase of temperature of hottest days is projected in some mid-latitude and semi-
10 arid regions, at about 1.5 time to twice the rate of global warming (high confidence). The highest increase of
11 temperature of coldest days is projected in Arctic regions, at about three times the rate of global warming
12 (high confidence). The probability of temperature extremes generally increases non-linearly with increasing
13 global warming levels (high confidence). Confidence in assessments depends on the spatial and temporal
14 scales of the extreme in question, with high confidence in projections of temperature-related extremes at
15 global and continental scales for daily to seasonal scales. There is high confidence that, on land, the
16 magnitude of temperature extremes increases more strongly than global mean temperature.
17
18
19 11.4 Heavy precipitation
20
21 This section assesses changes in heavy precipitation at global and regional scales. The main focus is on
22 extreme precipitation at a daily scale where literature is most concentrated, though extremes of shorter (sub-
23 daily) and longer (five-day or more) durations are also assessed to the extent the literature allows.
24
25
26 11.4.1 Mechanisms and drivers
27
28 SREX (Chapter 3, Seneviratne et al., 2012) assessed changes in heavy precipitation in the context of the
29 effects of thermodynamic and dynamic changes. Box 11.1 assesses thermodynamic and dynamic changes in
30 a warming world to aid the understanding of changes in observations and projections in some extremes and
31 the sources of uncertainties (See also Chapter 8, Section 8.2.3.2). In general, warming increases the
32 atmospheric water-holding capacity following the Clausius-Clapeyron (C-C) relation. This thermodynamic
33 effect results in an increase in extreme precipitation at a similar rate at the global scale. On a regional scale,
34 changes in extreme precipitation are further modulated by dynamic changes (Box 11.1).
35
36 Large-scale modes of variability, such as the North Atlantic Oscillation (NAO), El Niño-Southern
37 Oscillation (ENSO), Atlantic Multidecadal Variability (AMV), and Pacific Decadal Variability (PDV)
38 (Annex VI), modulate precipitation extremes through changes in environmental conditions or embedded
39 storms (Section 8.3.2). Latent heating can invigorate these storms (Nie et al., 2018; Zhang et al., 2019g);
40 changes in dynamics can increase precipitation intensity above that expected from the C-C scaling rate
41 (8.2.3.2, Box 11.1, and Section 11.7). Additionally, the efficiency of converting atmospheric moisture into
42 precipitation can change as a result of cloud microphysical adjustment to warming, resulting in changes in
43 the characteristics of extreme precipitation; but changes in precipitation efficiency in a warming world are
44 highly uncertain (Sui et al., 2020).
45
46 It is difficult to separate the effect of global warming from internal variability in the observed changes in the
47 modes of variability (Section 2.4). Future projections of modes of variability are highly uncertain (Section
48 4.3.3), resulting in uncertainty in regional projections of extreme precipitation. Future warming may amplify
49 monsoonal extreme precipitation. Changes in extreme storms, including tropical/extratropical cyclones and
50 severe convective storms, result in changes in extreme precipitation (Section 11.7). Also, changes in sea
51 surface temperatures (SSTs) alter land-sea contrast, leading to changes in precipitation extremes near coastal
52 regions. For example, the projected larger SST increase near the coasts of East Asia and India can result in
53 heavier rainfall near these coastal areas from tropical cyclones (Mei and Xie, 2016) or torrential rains
54 (Manda et al., 2014). The warming in the western Indian Ocean is associated with increases in moisture
55 surges on the low-level monsoon westerlies towards the Indian subcontinent, which may lead to an increase
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1 in the occurrence of precipitation extremes over central India (Krishnan et al., 2016; Roxy et al., 2017).
2
3 Decreases in atmospheric aerosols results in warming and thus an increase in extreme precipitation (Samset
4 et al., 2018; Sillmann et al., 2019). Changes in atmospheric aerosols also result in dynamic changes such as
5 changes in tropical cyclones (Takahashi et al., 2017; Strong et al., 2018). Uncertainty in the projections of
6 future aerosol emissions results in additional uncertainty in the heavy precipitation projections of the 21st
7 century (Lin et al., 2016).
8
9 There has been new evidence of the effect of local land use and land cover change on heavy precipitation.
10 There is a growing set of literature linking increases in heavy precipitation in urban centres to urbanization
11 (Argüeso et al., 2016; Zhang et al., 2019f). Urbanization intensifies extreme precipitation, especially in the
12 afternoon and early evening, over the urban area and its downwind region (medium confidence) (Box 10.3).
13 There are four possible mechanisms: a) increases in atmospheric moisture due to horizontal convergence of
14 air associated with the urban heat island effect (Shastri et al., 2015; Argüeso et al., 2016); b) increases in
15 condensation due to urban aerosol emissions (Han et al., 2011; Sarangi et al., 2017); c) aerosol pollution that
16 impacts cloud microphysics (Schmid and Niyogi, 2017) (Box 8.1); and d) urban structures that impede
17 atmospheric motion (Ganeshan and Murtugudde, 2015; Paul et al., 2018; Shepherd, 2013). Other local
18 forcing, including reservoirs (Woldemichael et al., 2012), irrigation (Devanand et al., 2019), or large-scale
19 land use and land cover change (Odoulami et al., 2019), can also affect local extreme precipitation.
20
21 In summary, precipitation extremes are controlled by both thermodynamic and dynamic processes.
22 Warming-induced thermodynamic change results in an increase in extreme precipitation, at a rate that
23 closely follows the Clausius-Clapeyron relationship at the global scale (high confidence). The effects of
24 warming-induced changes in dynamic drivers on extreme precipitation are more complicated, difficult to
25 quantify and are an uncertain aspect of projections. Precipitation extremes are also affected by forcings other
26 than changes in greenhouse gases, including changes in aerosols, land use and land cover change, and
27 urbanization (medium confidence).
28
29
30 11.4.2 Observed Trends
31
32 Both SREX (Chapter 3, Seneviratne et al., 2012) and AR5 (IPCC, 2014 Chapter 2) concluded it was likely
33 the number of heavy precipitation events over land had increased in more regions than it had decreased,
34 though there were wide regional and seasonal variations, and trends in many locations were not statistically
35 significant. This assessment has been strengthened with multiple studies finding robust evidence of the
36 intensification of extreme precipitation at global and continental scales, regardless of spatial and temporal
37 coverage of observations and the methods of data processing and analysis.
38
39 The average annual maximum precipitation amount in a day (Rx1day) has significantly increased since the
40 mid-20th century over land (Du et al., 2019; Dunn et al., 2020) and in the humid and dry regions of the globe
41 (Dunn et al., 2020). The percentage of observing stations with statistically significant increases in Rx1day is
42 larger than expected by chance, while the percentage of stations with statistically significant decreases is
43 smaller than expected by chance, over the global land as a whole and over North America, Europe, and Asia
44 (Figure 11.13, Sun et al., 2020) and over global monsoon regions (Zhang and Zhou, 2019) where data
45 coverage is relatively good. The addition of the past decade of observational data shows a more robust
46 increase in Rx1day over the global land region (Sun et al., 2020). Light, moderate, and heavy daily
47 precipitation has all intensified in a gridded daily precipitation data set (Contractor et al., 2020). Daily mean
48 precipitation intensities have increased since the mid-20th century in a majority of land regions (high
49 confidence, Section 8.3.1.3). The probability of precipitation exceeding 50 mm/day increased during 1961-
50 2018 (Benestad et al., 2019). The globally averaged annual fraction of precipitation from days in the top 5%
51 (R95pTOT) has also significantly increased (Dunn et al., 2020). The increase in the magnitude of Rx1day in
52 the 20th century is estimated to be at a rate consistent with C-C scaling with respect to global mean
53 temperature (Fischer and Knutti, 2016; Sun et al., 2020). Studies on past changes in extreme precipitation of
54 durations longer than a day are more limited, though there are some studies examining long-term trends in
55 annual maximum five-day precipitation (Rx5day). On global and continental scales, long-term changes in
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1 Rx5day are similar to those of Rx1day in many aspects (Zhang and Zhou 2019; Sun et al., 2020). As
2 discussed below, at the regional scale, changes in Rx5day are also similar to those of Rx1day where there are
3 analyses of changes in both Rx1day and Rx5day.
4
5 Overall, there is a lack of systematic analysis of long-term trends in sub-daily extreme precipitation at the
6 global scale. Often, sub-daily precipitation data have only sporadic spatial coverage and are of limited
7 length. Additionally, the available data records are far shorter than needed for a robust quantification of past
8 changes in sub-daily extreme precipitation (Li et al., 2018b). Despite these limitations, there are studies in
9 regions of almost all continents that generally indicate intensification of sub-daily extreme precipitation,
10 although confidence in an overall increase at the global scale remains very low. Studies include an increase in
11 extreme sub-daily rainfall in summer over South Africa (Sen Roy and Rouault, 2013), annually in Australia
12 (Guerreiro et al., 2018b), over 23 urban locations in India (Ali and Mishra, 2018), in Peninsular Malaysia
13 (Syafrina et al., 2015), and in eastern China in the summer season during 1971-2013 (Xiao et al., 2016). In
14 some regions in Italy (Arnone et al., 2013; Libertino et al., 2019) and in the US during 1950-2011 (Barbero
15 et al., 2017), there is also an increase. In general, an increase in sub-daily heavy precipitation results in an
16 increase in pluvial floods over smaller watersheds (Ghausi and Ghosh, 2020).
17
18 There is a considerable body of literature examining scaling of sub-daily precipitation extremes, conditional
19 on day-to-day air or dew-point temperatures (Westra et al., 2014; Fowler et al., 2021). This scaling, termed
20 apparent scaling (Fowler et al., 2020) is robust when different methodologies are used in different regions,
21 ranging between the C-C and two-times the C-C rate (e.g. Burdanowitz et al., 2019; Formayer and Fritz,
22 2017; Lenderink et al., 2017). This is confirmed when sub-daily precipitation data collected from multiple
23 continents (Lewis et al., 2019a) are analysed in a consistent manner using different methods (Ali et al.,
24 2021). It has been hoped that apparent scaling might be used to help understand past and future changes in
25 extreme sub-daily precipitation. However, apparent scaling samples multiple synoptic weather states, mixing
26 thermodynamic and dynamic factors that are not directly relevant for climate change responses (8.2.3.2)
27 (Prein et al., 2016b; Bao et al., 2017; Zhang et al., 2017c; Drobinski et al., 2018; Sun et al., 2019d). The
28 spatial pattern of apparent scaling is different from those of projected changes over Australia (Bao et al.,
29 2017) and North America (Sun et al., 2019) in regional climate model simulations. It thus remains difficult to
30 use the knowledge about apparent scaling to infer past and future changes in extreme sub-daily precipitation
31 according to observed and projected changes in local temperature.
32
33 In Africa (Table 11.5), evidence shows an increase in extreme daily precipitation for the late half of the 20th
34 century over the continent where data are available; there is a larger percentage of stations showing
35 significant increases in extreme daily precipitation than decreases (Sun et al., 2020). There are increases in
36 different metrics relevant to extreme precipitation in various regions of the continent (Chaney et al., 2014;
37 Harrison et al., 2019; Dunn et al., 2020; Sun et al., 2020). There is an increase in extreme precipitation
38 events in southern Africa (Weldon and Reason, 2014; Kruger et al., 2019) and a general increase in heavy
39 precipitation over East Africa, the Greater Horn of Africa (Omondi et al., 2014). Over sub-Saharan Africa,
40 increases in the frequency and intensity of extreme precipitation have been observed over the well-gauged
41 areas during 1950-2013; however, this covers only 15% of the total area of sub-Saharan Africa (Harrison et
42 al., 2019). Confidence about the increase in extreme precipitation for some regions where observations are
43 more abundant is medium, but for Africa as whole, it is low because of a general lack of continent-wide
44 systematic analysis, the sporadic nature of available precipitation data over the continent, and spatially non-
45 homogenous trends in places where data are available (Donat et al., 2014a; Mathbout et al., 2018; Alexander
46 et al., 2019; Funk et al., 2020)
47
48 In Asia (Table 11.8), there is robust evidence that extreme precipitation has increased since the 1950s (high
49 confidence), however this is dominated by high spatial variability. Increases in Rx1day and Rx5day during
50 1950-2018 are found over two thirds of stations and the percentage of stations with statistically significant
51 trends is significantly larger than can be expected by chance (Sun et al., 2020, also Fig 11.13). An increase
52 in extreme precipitation has also been observed in various regional studies based on different metrics of
53 extreme precipitation and different spatial and temporal coverage of the data. These include an increase in
54 daily precipitation extremes over central Asia (Hu et al., 2016), most of South Asia (Zahid and Rasul, 2012;
55 Pai et al., 2015; Sheikh et al., 2015; Adnan et al., 2016; Malik et al., 2016; Dimri et al., 2017; Priya et al.,
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1 2017; Roxy et al., 2017; Hunt et al., 2018; Kim et al., 2019; Wester et al., 2019), the Arabian Peninsula
2 (Rahimi and Fatemi, 2019; Almazroui and Saeed, 2020; Atif et al., 2020), Southeast Asia (Siswanto et al.,
3 2015; Supari et al., 2017; Cheong et al., 2018); the northwest Himalaya (Malik et al., 2016), parts of east
4 Asia (Nayak et al., 2017; Baek et al., 2017; Ye and Li, 2017), the western Himalayas since the 1950s (Ridley
5 et al., 2013; Dimri et al., 2015; Madhura et al., 2015), WSB, ESB and RFE (Donat et al., 2016a) and a
6 decrease was found over the eastern Himalayas (Sheikh et al., 2015; Talchabhadel et al., 2018). Increases
7 have been observed over Jakarta (Siswanto et al., 2015), but Rx1day over most parts of the Maritime
8 Continent has decreased (Villafuerte and Matsumoto, 2015). Trends in extreme precipitation over China are
9 mixed with increases and decreases (Fu et al., 2013a; Jiang et al., 2013; Ma et al., 2015; Yin et al., 2015;
10 Xiao et al., 2016) and are not significant over China as whole (Li et al., 2018c; Ge et al., 2017; Hu et al.,
11 2016; Jiang et al., 2013; Liu et al., 2019b; Chen et al., 2021; Deng et al., 2018; He and Zhai, 2018; Tao et al.,
12 2018). With few exceptions, most Southeast Asian countries have experienced an increase in rainfall
13 intensity, but with a reduced number of wet days (Donat et al., 2016a; Cheong et al., 2018; Naveendrakumar
14 et al., 2019), though large differences in trends exists if the trends are estimated from different datasets
15 including gauge-based, remotely-sensed, and reanalysis over a relatively short period (Kim et al. 2019).
16 There is a significant increase in heavy rainfall (>100 mm day-1) and a significant decrease in moderate
17 rainfall (5–100 mm day-1) in central India during the South Asian monsoon season (Deshpande et al., 2016;
18 Roxy et al., 2017).
19
20 In Australasia (Table 11.11), available evidence has not shown an increase or a decrease in heavy
21 precipitation over Australasia as a whole (medium confidence), but heavy precipitation tends to increase over
22 northern Australia (particularly the northwest) and decrease over the eastern and southern regions (e.g.,
23 Jakob and Walland, 2016; Dey et al., 2018; Guerreiro et al., 2018; Dunn et al., 2020; Sun et al., 2020).
24 Available studies that used long-term observations since the mid-20th century showed nearly as many
25 stations with an increase as those with a decrease in heavy precipitation (Jakob and Walland, 2016) or
26 slightly more stations with a decrease than with an increase in Rx1day and Rx5day (Sun et al., 2020), or
27 strong differences in Rx1day trends with increases over northern Australia and central Australia in general
28 but mostly decreases over southern Australia and eastern Australia (Dunn et al., 2020). Over New Zealand,
29 decreases are observed for moderate-heavy precipitation events, but there are no significant trends for very
30 heavy events (more than 64 mm in a day) for the period 1951-2012. The number of stations with an increase
31 in very wet days is similar to that with a decrease during 1960-2019 (MfE and Stats NZ, 2020). Overall,
32 there is low confidence in trends in the frequency of heavy rain days with mostly decreases over New
33 Zealand (Caloiero, 2015; Harrington and Renwick, 2014).
34
35 In Central and South America (Table 11.14), evidence shows an increase in extreme precipitation, but in
36 general there is low confidence; while continent-wide analyses produced wetting trends, trends are not
37 robust. Rx1day increased at more stations than it decreased in South America between 1950-2018 (Sun et al.,
38 2020). Over 1950-2010, both Rx5day and R99p increased over large regions of South America, including
39 NWS, NSA, and SES (Skansi et al., 2013). There are large regional differences. A decrease in daily extreme
40 precipitation is observed in northeastern Brazil (Bezerra et al., 2018; Dereczynski et al., 2020; Skansi et al.,
41 2013). Trends in extreme precipitation indices were not statistically significant over the period 1947-2012
42 within the São Francisco River basin in the Brazilian semi-arid region (Bezerra et al., 2018). An increase in
43 extreme rainfall is observed in AMZ with medium confidence (Skansi et al., 2013) and in SES with high
44 confidence (Ávila et al., 2016; Barros et al., 2015; Lovino et al., 2018; Skansi et al., 2013; Wu and Polvani,
45 2017; Dereczynski et al., 2020; Valverde and Marengo, 2014). Among all sub-regions, SES shows the
46 highest rate of increase for rainfall extremes, followed by AMZ (Skansi et al., 2013). Increases in the
47 intensity of heavy daily rainfall events have been observed in the southern Pacific and in the Titicaca basin
48 (Huerta and Lavado-Casimiro, 2020; Skansi et al., 2013). In SCA trends in annual precipitation are generally
49 not significant, although small (but significant) increases are found in Guatemala, El Salvador, and Panama
50 (Hidalgo et al., 2017). Small positive trends were found in multiple extreme precipitation indices over the
51 Caribbean region over a short time period (1986-2010) (Stephenson et al., 2014; McLean et al., 2015)
52
53 In Europe (Table 11.17), there is robust evidence that the magnitude and intensity of extreme precipitation
54 has very likely increased since the 1950s. There is a significant increase in Rx1day and Rx5day during 1950-
55 2018 in Europe as whole (Sun et al., 2020, also Figure 11.13). The number of stations with increases far
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1 exceeds those with decreases in the frequency of daily rainfall exceeding its 90th or 95th percentile in century-
2 long series (Cioffi et al., 2015). The 5-, 10-, and 20-year events of one-day and five-day precipitation during
3 1951-1960 became more common since the 1950s (van den Besselaar et al., 2013). There can be large
4 discrepancies among studies and regions and seasons (Croitoru et al., 2013; Willems, 2013; Casanueva et al.,
5 2014; Roth et al., 2014; Fischer et al., 2015); evidence for increasing extreme precipitation is more
6 frequently observed for summer and winter, but not in other seasons (Madsen et al., 2014; Helama et al.,
7 2018). An increase is observed in central Europe (Volosciuk et al., 2016; Zeder and Fischer, 2020), and in
8 Romania (Croitoru et al., 2016). Trends in the Mediterranean region are in general not spatially (Reale and
9 Lionello, 2013), with decreases in the western Mediterranean and some increases in the eastern
10 Mediterranean (Rajczak et al., 2013; Casanueva et al., 2014; de Lima et al., 2015; Gajić-Čapka et al., 2015;
11 Sunyer et al., 2015; Pedron et al., 2017; Serrano-Notivoli et al., 2018; Ribes et al., 2019). In the Netherlands,
12 the total precipitation contributed from extremes higher than the 99th percentile doubles per degree C
13 increase in warming (Myhre et al., 2019), though extreme rainfall trends in northern Europe may differ in
14 different seasons (Irannezhad et al., 2017).
15
16 In North America (Table 11.20), there is robust evidence that the magnitude and intensity of extreme
17 precipitation has very likely increased since the 1950s. Both Rx1day and Rx5day have significantly increased
18 in North America during 1950-2018 (Sun et al., 2020, also Figure 11.13). There is, however, regional
19 diversity. In Canada, there is a lack of detectable trends in observed annual maximum daily (or shorter
20 duration) precipitation (Shephard et al., 2014; Mekis et al., 2015; Vincent et al., 2018). In the United States,
21 there is an overall increase in one-day heavy precipitation, both in terms of intensity and frequency (Sun et
22 al., 2020; Donat et al., 2013; Huang et al., 2017; Villarini et al., 2012; Easterling et al., 2017; Wu, 2015;
23 Howarth et al., 2019), except for the southern part of the US (Hoerling et al., 2016) where internal variability
24 may have played a substantial role in the lack of observed increases. In Mexico, increases are observed in
25 R10mm and R95p (Donat et al., 2016a), very wet days over the cities (García-Cueto et al., 2019) and in
26 PRCPTOT and Rx1day (Donat et al., 2016b).
27
28 In Small Islands, there is a lack of evidence showing changes in heavy precipitation overall. There were
29 increases in extreme precipitation in Tobago from 1985–2015 (Stephenson et al., 2014; Dookie et al., 2019)
30 and decreases in southwestern French Polynesia and the southern subtropics (low confidence; Atlas.10; Table
31 11.5). Extreme precipitation leading to flooding in the small islands has been attributed in part to TCs, as
32 well as being influenced by ENSO (Khouakhi et al., 2016; Hoegh-Guldberg et al., 2018) (Box 11.5).
33
34
35 [START FIGURE 11.13 HERE]
36
37 Figure 11.13:Signs and significance of the observed trends in annual maximum daily precipitation (Rx1day) during
38 1950–2018 at 8345 stations with sufficient data. (a) Percentage of stations with statistically significant
39 trends in Rx1day; green dots show positive trends and brown dots negative trends. Box-and-whisker plots
40 indicate the expected percentage of stations with significant trends due to chance estimated from 1000
41 bootstrap realizations under a no-trend null hypothesis. The boxes mark the median, 25th percentile, and
42 75th percentile. The upper and lower whiskers show the 97.5th and the 2.5th percentiles, respectively. Maps
43 of stations with positive (b) and negative (c) trends. The light color indicates stations with non-significant
44 trends and the dark color stations with significant trends. Significance is determined by a two-tailed test
45 conducted at the 5% level. Adapted from Sun et al., (2020). © American Meteorological Society. Used
46 with permission. Further details on data sources and processing are available in the chapter data table
47 (Table 11.SM.9).
48
49 [END FIGURE 11.13 HERE]
50
51
52 In summary, the frequency and intensity of heavy precipitation have likely increased at the global scale over
53 a majority of land regions with good observational coverage. Since 1950, the annual maximum amount of
54 precipitation falling in a day or over five consecutive days has likely increased over land regions with
55 sufficient observational coverage for assessment, with increases in more regions than there are decreases.
56 Heavy precipitation has likely increased on the continental scale over three continents, including North
57 America, Europe, and Asia where observational data are more abundant. There is very low confidence about
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1 changes in sub-daily extreme precipitation due to a limited number of studies and the data used in these
2 studies are often limited.
3
4
5 11.4.3 Model evaluation
6
7 The evaluation of the skill of climate models to simulate heavy precipitation extremes is challenging due to a
8 number of factors, including the lack of reliable observations and the spatial scale mismatch between
9 simulated and observed data (Avila et al., 2015, Alexander et al., 2019). Simulated precipitation represents
10 areal means, but station-based observations are conducted at point locations and are often sparse. The areal-
11 reduction factor, the ratio between pointwise station estimates of extreme precipitation and extremes of the
12 areal mean, can be as large as 130% at CMIP6 resolutions (~100km) (Gervais et al., 2014). Hence, the order
13 in which gridded station based extreme values are constructed (i.e., if the extreme values are extracted at the
14 station first and then gridded or if the daily station values are gridded and then the extreme values are
15 extracted) represents different spatial scales of extreme precipitation and needs to be taken into account in
16 model evaluation (Wehner et al. 2020). This aspect has been considered in some studies. Reanalysis products
17 are used in place of station observations for their spatial completeness as well as spatial-scale comparability
18 (Sillmann et al., 2013a; Kim et al., 2020; Li et al., 2020). However, reanalyses share similar
19 parameterizations to the models themselves, reducing the objectivity of the comparison.
20
21 Different generations of the Coupled Model Intercomparison Project (CMIP) models have improved over
22 time, though quite modestly (Flato et al., 2013; Watterson et al., 2014). Improvements in the representation
23 of the magnitude of the ETCCDI indices in CMIP5 over CMIP3 (Sillmann et al., 2013a; Chen and Sun,
24 2015a) have been attributed to higher resolution as higher-resolution models represent smaller areas at
25 individual grid boxes. Additionally, the spatial distribution of extreme rainfall simulated by high-resolution
26 models (CMIP5 median resolution ∼ 180 ×96) is generally more comparable to observations (Sillmann et al.,
27 2013b; Kusunoki, 2017, 2018b; Scher et al., 2017) as these models tend to produce more realistic storms
28 compared to coarser models (11.7.2). Higher horizontal resolution alone improves simulation of extreme
29 precipitation in some models (Wehner et al., 2014; Kusunoki, 2017, 2018), but this is insufficient in other
30 models (Bador et al., 2020) as model parameterization also plays a significant role (Wu et al., 2020a). A
31 simple comparison of climatology may not fully reflect the improvements of the new models that have more
32 comprehensive formulations of processes (Di Luca et al., 2015). Dittus et al. (2016) found that many of the
33 eight CMIP5 models they evaluated reproduced the observed increase in the difference between areas
34 experiencing an extreme high (90%) and an extreme low (10%) proportion of the annual total precipitation
35 from heavy precipitation (R95p/PRCPTOT) for Northern Hemisphere regions. Additionally, CMIP5 models
36 reproduced the relation between changes in extreme and non-extreme precipitation: an increase in extreme
37 precipitation is at the cost of a decrease in non-extreme precipitation (Thackeray et al., 2018), a characteristic
38 found in the observational record (Gu and Adler, 2018).
39
40 CMIP6 models perform reasonably well in capturing large-scale features of precipitation extremes, including
41 intense precipitation extremes in the intertropical convergence zone (ITCZ), and weak precipitation extremes
42 in dry areas in the tropical regions (Li et al., 2020) but a double-ITCZ bias over the equatorial central and
43 eastern Pacific that appeared in CMIP5 models remains (3.3.2.1). There are also regional biases in the
44 magnitude of precipitation extremes (Kim et al., 2020). The models also have difficulties in reproducing
45 detailed regional patterns of extreme precipitation such as over the northeast US (Agel and Barlow, 2020),
46 though they performed better for summer extremes over the US (Akinsanola et al., 2020). The comparison
47 between climatologies in the observations and in model simulations shows that the CMIP6 and CMIP5
48 models that have similar horizontal resolutions also have similar model evaluation scores and their error
49 patterns are highly correlated (Wehner et al., 2020). In general, extreme precipitation in CMIP6 models tends
50 to be somewhat larger than in CMIP5 models (Li et al., 2020a), reflecting smaller spatial scales of extreme
51 precipitation represented by slightly higher resolution models (Gervais et al., 2014). This is confirmed by
52 Kim et al. (2020), who showed that Rx1day and Rx5day simulated by CMIP6 models tend to be closer to
53 point estimates of HadEX3 data (Dunn et al., 2020) than those simulated by CMIP5. Figure 11.14 shows the
54 multi-model ensemble bias in mean Rx1day over the period 1979-2014 from 21 available CMIP6 models
55 when compared with observations and reanalyses. Measured by global land root mean square error, the
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Final Government Distribution Chapter11 IPCC AR6 WGI
1 model performance is generally consistent across different observed/reanalysis data products for the extreme
2 precipitation metric (Figure 11.14). The magnitude of extreme area-mean precipitation simulated by the
3 CMIP6 models is consistently smaller than the point estimates of HadEX3, but the model values are more
4 comparable to those of areal-mean values (Figure 11.14) of the ERA5 reanalysis or REGEN (Contractor et
5 al., 2020b). Taylor-plot-based performance metrics reveal strong similarities in the patterns of extreme
6 precipitation errors over land regions between CMIP5 and CMIP6 (Srivastava et al., 2020; Wehner et al.,
7 2020) and between annual mean precipitation errors and Rx1day errors for both generations of models
8 (Wehner et al., 2020).
9
10 In general, there is high confidence that historical simulations by CMIP5 and CMIP6 models of similar
11 horizontal resolutions are interchangeable in their performance in simulating the observed climatology of
12 extreme precipitation.
13
14
15 [START FIGURE 11.14 HERE]
16
17 Figure 11.14:Multi-model mean bias in annual maximum daily precipitation (Rx1day, %) for the period 1979-2014,
18 calculated as the difference between the CMIP6 multi-model mean and the average of available
19 observational or reanalysis products including (a) ERA5, (b) HadEX3, and (c) and REGEN. Bias is
20 expressed as the percent error relative to the long-term mean of the respective observational data
21 products. Brown indicates that models are too dry, while green indicates that they are too wet. Areas
22 without sufficient observational data are shown in grey. Adapted from Wehner et al. (2020) under the
23 terms of the Creative Commons Attribution license. Further details on data sources and processing are
24 available in the chapter data table (Table 11.SM.9).
25
26 [END FIGURE 11.14 HERE]
27
28
29 Studies using regional climate models (RCMs), for example, CORDEX (Giorgi et al., 2009) over Africa
30 (Dosio et al., 2015; Klutse et al., 2016; Pinto et al., 2016; Gibba et al., 2019), Australia, East Asia (Park et
31 al., 2016), Europe (Prein et al., 2016a; Fantini et al., 2018), and parts of North America (Diaconescu et al.,
32 2018) suggest that extreme rainfall events are better captured in RCMs compared to their host GCMs due to
33 their ability to address regional characteristics, for example, topography and coastlines. However, CORDEX
34 simulations do not show good skill over south Asia for heavy precipitation and do not add value with respect
35 to their GCM source of boundary conditions (Mishra et al., 2014a; Singh et al., 2017b). The evaluation of
36 models in simulating regional processes is discussed in detail in Chapter 10 (Section 10.3.3.4). The high-
37 resolution simulation of mid-latitude winter extreme precipitation over land is of similar magnitude to point
38 observations. Simulation of summer extreme precipitation has a high bias when compared with observations
39 at the same spatial scale. Simulated extreme precipitation in the tropics also appears to be too large,
40 indicating possible deficiencies in the parameterization of cumulus convection at this resolution. Indeed,
41 precipitation distributions at both daily and sub-daily time scales are much improved with a convection-
42 permitting model (Belušić et al., 2020) over west Africa (Berthou et al., 2019b), East Africa (Finney et al.,
43 2019), North America and Canada (Cannon and Innocenti, 2019; Innocenti et al., 2019) and over Belgium in
44 Europe (Vanden Broucke et al., 2019).
45
46 In summary, there is high confidence in the ability of models to capture the large-scale spatial distribution of
47 precipitation extremes over land. The magnitude and frequency of extreme precipitation simulated by
48 CMIP6 models are similar to those simulated by CMIP5 models (high confidence).
49
50
51 11.4.4 Detection and attribution, event attribution
52
53 Both SREX (Chapter 3, Seneviratne et al., 2012) and AR5 (Chapter 10, IPCC, 2014) concluded with medium
54 confidence that anthropogenic forcing has contributed to a global-scale intensification of heavy precipitation
55 over the second half of the 20th century. These assessments were based on the evidence of anthropogenic
56 influence on aspects of the global hydrological cycle, in particular, the human contribution to the warming-
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1 induced observed increase in atmospheric moisture that leads to an increase in heavy precipitation, and
2 limited evidence of anthropogenic influence on extreme precipitation of durations of one and five days.
3
4 Since AR5 there has been new and robust evidence and improved understanding of human influence on
5 extreme precipitation. In particular, detection and attribution analyses have provided consistent and robust
6 evidence of human influence on extreme precipitation of one- and five-day durations at global to continental
7 scales. The observed increases in Rx1day and Rx5day over the Northern Hemisphere land area during 1951-
8 2005 can be attributed to the effect of combined anthropogenic forcing, including greenhouse gases and
9 anthropogenic aerosols, as simulated by CMIP5 models and the rate of intensification with regard to
10 warming is consistent with C-C scaling (Zhang et al., 2013). This is confirmed to be robust when an
11 additional nine years of observational data and the CMIP6 model simulations were used (Paik et al., 2020;
12 CCB3.2, Figure 1). Additionally, the influence of greenhouse gases is attributed as the dominant contributor
13 to the observed intensification. The global average of Rx1day in the observations is consistent with
14 simulations by both CMIP5 and CMIP6 models under anthropogenic forcing, but not under natural forcing
15 (CCB3.2, Figure 1). The observed increase in the fraction of annual total precipitation falling into the top 5th
16 or top 1st percentiles of daily precipitation can also be attributed to human influence at the global scale (Dong
17 et al., 2020). CMIP5 models were able to capture the fraction of land experiencing a strong intensification of
18 heavy precipitation during 1960-2010 under anthropogenic forcing, but not in unforced simulations (Fischer
19 et al., 2014)). But the models underestimated the observed trends (Borodina et al., 2017a). Human influence
20 also significantly contributed to the historical changes in record-breaking one-day precipitation (Shiogama et
21 al., 2016). There is also limited evidence of the influences of natural forcing. Substantial reductions in
22 Rx5day and SDII (daily precipitation intensity) over the global summer monsoon regions occurred during
23 1957-2000 after explosive volcanic eruptions (Paik and Min, 2018). The reduction in post-volcanic eruption
24 extreme precipitation in the simulations is closely linked to the decrease in mean precipitation, for which
25 both thermodynamic effects (moisture reduction due to surface cooling) and dynamic effects (monsoon
26 circulation weakening) play important roles.
27
28 There has been new evidence of human influence on extreme precipitation at continental scales, including
29 the detection of the combined effect of greenhouse gases and aerosol forcing on Rx1day and Rx5day over
30 North America, Eurasia, and mid-latitude land regions (Zhang et al., 2013) and of greenhouse gas forcing in
31 Rx1day and Rx5day in the mid-to-high latitudes, western and eastern Eurasia, and the global dry regions
32 (Paik et al., 2020). These findings are corroborated by the detection of human influence in the fraction of
33 extreme precipitation in the total precipitation over Asia, Europe, and North America (Dong et al., 2020).
34 Human influence was found to have contributed to the increase in frequency and intensity of regional
35 precipitation extremes in North America during 1961-2010, based on both optimal fingerprinting and event
36 attribution approaches (Kirchmeier-Young and Zhang, 2020). Tabari et al. (2020) found the observed
37 latitudinal increase in extreme precipitation over Europe to be consistent with model-simulated responses to
38 anthropogenic forcing.
39
40 Evidence of human influence on extreme precipitation at regional scales is more limited and less robust. In
41 northwest Australia, the increase in extreme rainfall since 1950 can be related to increased monsoonal flow
42 due to increased aerosol emissions, but cannot be attributed to an increase in greenhouse gases (Dey et al.,
43 2019a). Anthropogenic influence on extreme precipitation in China was detected in one study (Li et al.,
44 2017), but it was not detected in another study (Li et al., 2018e) using different detection and data-processing
45 procedures, indicating the lack of robustness in the detection results. A still weak signal-to-noise ratio seems
46 to be the main cause for the lack of robustness, as detection would become robust 20 years in the future (Li
47 et al., 2018e). Krishnan et al. (2016) attributed the observed increase in heavy rain events (intensity > 100
48 mm/day) in the post-1950s over central India to the combined effects of greenhouse gases, aerosols, land use
49 and land cover changes, and rapid warming of the equatorial Indian Ocean SSTs. Roxy et al. (2017) and
50 Devanand et al. (2019) showed the increase in widespread extremes over the South Asian Monsoon during
51 1950-2015 is due to the combined impacts of the warming of the Western Indian Ocean (Arabian Sea) and
52 the intensification of irrigation water management over India
53
54 Anthropogenic influence may have affected the large-scale meteorological processes necessary for extreme
55 precipitation and the localized thermodynamic and dynamic processes, both contributing to changes in
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1 extreme precipitation events. Several new methods have been proposed to disentangle these effects by either
2 conditioning on the circulation state or attributing analogues. In particular, the extremely wet winter of
3 2013/2014 in the UK can be attributed, approximately to the same degree, to both temperature-induced
4 increases in saturation vapour pressure and changes in the large-scale circulation (Vautard et al., 2016; Yiou
5 et al., 2017). There are multiple cases indicating that very extreme precipitation may increase at a rate more
6 than the C-C rate (6-7%/ °C) (Pall et al., 2017; Risser and Wehner, 2017; van der Wiel et al., 2017; van
7 Oldenborgh et al., 2017;Wang et al., 2018).
8
9 Event attribution studies found an influence of anthropogenic activities on the probability or magnitude of
10 observed extreme precipitation events, including European winters (Schaller et al., 2016; Otto et al., 2018b),
11 extreme 2014 precipitation over the northern Mediterranean (Vautard et al., 2015), parts of the US for
12 individual events (Knutson et al., 2014b; Szeto et al., 2015; Eden et al., 2016; van Oldenborgh et al., 2017),
13 extreme rainfall in 2014 over Northland, New Zealand (Rosier et al., 2016) or China (Burke et al., 2016; Sun
14 and Miao, 2018; Yuan et al., 2018b; Zhou et al., 2018). For other heavy rainfall events, however, studies
15 identified a lack of evidence about anthropogenic influences (Imada et al., 2013; Schaller et al., 2014; Otto et
16 al., 2015c; Siswanto et al., 2015). There are also studies whose results are inconclusive because of limited
17 reliable simulations (Christidis et al., 2013b; Angélil et al., 2016). Overall, both the spatial and temporal
18 scales on which extreme precipitation events are defined are important for attribution; events defined on
19 larger scales have larger signal-to-noise ratios and thus the signal is more readily detectable. At the current
20 level of global warming, there is a strong enough signal to be detectable for large-scale extreme precipitation
21 events, but the chance to detect such signals for smaller-scale events becomes smaller (Kirchmeier‐Young et
22 al., 2019).
23
24 In summary, most of the observed intensification of heavy precipitation over land regions is likely due to
25 anthropogenic influence, for which greenhouse gases emissions are the main contributor. New and robust
26 evidence since AR5 includes attribution of the observed increase in annual maximum one-day and five-day
27 precipitation and in the fraction of annual precipitation due to heavy events to human influence. It also
28 includes a larger fraction of land showing enhanced extreme precipitation and a larger probability of record-
29 breaking one-day precipitation than expected by chance, both of which can only be explained when
30 anthropogenic greenhouse gas forcing is considered. Human influence has contributed to the intensification
31 of heavy precipitation in three continents where observational data are more abundant, including North
32 America, Europe and Asia (high confidence). On the spatial scale of AR6 regions, evidence of human
33 influence on extreme precipitation is limited, but new evidence is emerging; in particular, studies attributing
34 individual heavy precipitation events found that human influence was a significant driver of the events,
35 particularly in the winter season.
36
37
38 11.4.5 Projections
39
40 AR5 concluded it is very likely that extreme precipitation events will be more frequent and more intense over
41 most of the mid-latitude land masses and wet tropics in a warmer world (Collins et al., 2013). Post-AR5
42 studies provide more and robust evidence to support the previous assessments. These include an observed
43 increase in extreme precipitation (11.4.3) and human causes of past changes (11.4.4), as well as projections
44 based on either GCM and/or RCM simulations. CMIP5 models project the rate of increase in Rx1day with
45 warming is independent of the forcing scenario (Pendergrass et al., 2015, Chapter 8, Section 8.5.3.1) or
46 forcing mechanism (Sillmann et al., 2017). This is confirmed in CMIP6 simulations (Li et al., 2020, and
47 Sillmann et al., 2019). In particular, for extreme precipitation that occurs once a year or less frequently, the
48 magnitudes of the rates of change per 1°C change in global mean temperature are similar regardless of
49 whether the temperature change is caused by increases in CO2, CH4, solar forcing, or SO4 (Sillmann et al.,
50 2019). In some models, CESM1 in particular, the extreme precipitation response to warming may follow a
51 quadratic relation (Pendergrass et al., 2019). Figure 11.15 shows changes in the 10-year and 50-year return
52 values of Rx1day at different warming levels as simulated by the CMIP6 models. The median value of the
53 scaling over land, across all SSP scenarios and all models, is close to 7%/°C for the 50-year return value of
54 Rx1day. It is just slightly smaller for the 10-year and 50-year return values of Rx5day (Li et al., 2020a). The
55 90% ranges of the multi-model ensemble changes across all land grid boxes in the 50-yr return values for
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1 Rx1day and Rx5day do not overlap between 1.5°C and 2°C warming levels (Li et al., 2020), indicating that a
2 small increment such as 0.5°C in global warming can result in a significant increase in extreme precipitation.
3 Projected long-period Rx1day return value changes are larger than changes in mean Rx1day and increase
4 with increasing rarity (Pendergrass, 2018; Mizuta and Endo, 2020; Wehner, 2020). The rate of change of
5 moderate extreme precipitation may depend more on the forcing agent, similar to the mean precipitation
6 response to warming (Lin et al., 2016, 2018a). Thus, there is high confidence that extreme precipitation that
7 occurs once a year or less frequently increases proportionally to the amount of surface warming and the rate
8 of change in precipitation is not dependent on the underlying forcing agents of warming.
9
10
11 [START FIGURE 11.15 HERE]
12
13
14 Figure 11.15:Projected changes in the intensity of extreme precipitation events under 1°C, 1.5°C, 2°C, 3°C, and 4°C
15 global warming levels relative to the 1851-1900 baseline. Extreme precipitation events are defined as the
16 daily precipitation (Rx1day) that was exceeded on average once during a 10-year period (10-year event,
17 blue) and once during a 50-year period (50-year event, orange) during the 1851-1900 base period. Results
18 are shown for the global land. For each box plot, the horizontal line and the box represent the median and
19 central 66% uncertainty range, respectively, of the intensity changes across the space, and the whiskers
20 extend to the 90% uncertainty range. The results are based on the multi-model ensemble median
21 estimated from simulations of global climate models contributing to the sixth phase of the Coupled Model
22 Intercomparison Project (CMIP6) under different SSP forcing scenarios. Based on (Li et al., 2020a).
23 Further details on data sources and processing are available in the chapter data table (Table 11.SM.9).
24
25
26 [END FIGURE 11.15 HERE]
27
28
29 The spatial patterns of the projected changes across different warming levels are quite similar, as shown in
30 Figure 11.16 and confirmed by near-linear scaling between extreme precipitation and global warming levels
31 at regional scales (Seneviratne and Hauser, 2020). Internal variability modulates changes in heavy rainfall
32 (Wood and Ludwig, 2020), resulting in different changes in different regions (Seneviratne and Hauser,
33 2020). Extreme precipitation nearly always increases across land areas with larger increases at higher global
34 warming levels, except in very few regions, such as southern Europe around the Mediterranean Basin in
35 some seasons. The very likely ranges of the multi-model ensemble changes across all land grid boxes in the
36 50-yr return values for Rx1day and Rx5day between 1.5°C and 1°C warming levels are above zero for all
37 continents expect Europe, with likely range above zero over Europe (Li et al., 2020). Decreases in extreme
38 precipitation are confined mostly to subtropical ocean areas and are highly correlated to decreases in mean
39 precipitation due to storm track shifts. These subtropical decreases can extend to nearby land areas in
40 individual realizations.
41
42 Projected increases in the probability of extreme precipitation of fixed magnitudes are non-linear and show
43 larger increases for more rare events (Figures 11.7 and 11.15, Fischer and Knutti, 2015, Li et al., 2020,
44 Kharin et al., 2018). CMIP5-model-projected increases in the probability of high (99th and 99.9th) percentile
45 precipitation between 1.5°C and 2°C warming scenarios are consistent with what can be expected based on
46 observed changes (Fischer and Knutti, 2015), providing confidence in the projections. CMIP5 model
47 simulations show that the frequency for present-day climate 20-year extreme precipitation is projected to
48 increase by 10% at the 1.5°C global warming level and by 22% at the 2.0°C global warming level, while the
49 increase in the frequency for present-day climate 100-year extreme precipitation is projected to increase by
50 20% and more than 45% at the 1.5°C and 2.0°C warming levels, respectively (Kharin et al., 2018). CMIP6
51 simulations with SSP scenarios show the frequency of 10-year and 50-year events will be approximately
52 doubled and tripled, respectively, at a very high warming level of 4°C (Figure 11.7, Li et al., 2020).
53
54 The number of studies on the projections of extreme hourly precipitation are limited. The ability of GCMs to
55 simulate hourly precipitation extremes is limited (Morrison et al., 2019) and very few modelling centres
56 archive sub-daily and hourly precipitation prior to CMIP6 experiments. RCM simulations project an increase
57 in extreme sub-daily precipitation in North America (Li et al., 2019a) and over Sweden (Olsson and Foster,
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1 2013), but these models still do not explicitly resolve convective processes that are important for properly
2 simulating extreme sub-daily precipitation. Simulations by RCMs that explicitly resolve convective
3 processes (convection-permitting models) are limited in length and only available in a few regions because
4 of high computing costs. Yet, a majority of the available convection-permitting simulations project increases
5 in the intensities of extreme sub-daily precipitation events with the amount similar to or higher than the C-C
6 scaling rate (Ban et al., 2015; Helsen et al., 2020; Kendon et al., 2014, 2019; Prein et al., 2016b; Fowler et
7 al., 2020). An increase is projected in extreme sub-daily precipitation over Africa (Kendon et al., 2019); over
8 East Africa (Finney et al., 2020) and West Africa (Berthou et al., 2019a; Fitzpatrick et al., 2020), even for
9 areas where parameterized RCMs project a decrease; in Europe (Chan et al., 2020 and Hodnebrog et al.,
10 2019); as well as in the continental US (Prein et al., 2016). Overall, available evidence, while limited, points
11 to an increase in extreme sub-daily precipitation in the future. Studies on future changes in extreme
12 precipitation for a month or longer are limited. One study projects an increase in extreme monthly
13 precipitation in Japan under 4°C global warming for around 80% of stations in the summer (Hatsuzuka and
14 Sato, 2019).
15
16 In Africa (Table 11.5), extreme precipitation will likely increase under warming levels of 2°C or below
17 (compared to pre-industrial values) and very likely increase at higher warming levels. Simulations by
18 CMIP5, CMIP6 and CORDEX regional models project an increase in daily extreme precipitation between
19 1.5°C and 2.0°C warming levels. The pattern of change in heavy precipitation under different scenarios or
20 warming levels is similar with larger increases for higher warming levels (e.g., Nikulin et al., 2018; Li et al.,
21 2020). With increases in warming, extreme precipitation is projected to increase in the majority of land
22 regions in Africa (Mtongori et al., 2016; Pfahl et al., 2017; Diedhiou et al., 2018; Dunning et al., 2018;
23 Akinyemi and Abiodun, 2019; Giorgi et al., 2019). Over southern Africa, heavy precipitation will likely
24 increase by the end of the 21st century under RCP 8.5 (Dosio, 2016; Pinto et al., 2016; Abiodun et al., 2017;
25 Dosio et al., 2019). However, heavy rainfall amounts are projected to decrease over western South Africa
26 (Pinto et al., 2018) as a result of a projected decrease in the frequency of the prevailing westerly winds south
27 of the continent that translates into fewer cold fronts and closed mid-latitudes cyclones (Engelbrecht et al.,
28 2009; Pinto et al., 2018). Heavy precipitation will likely increase by the end of the century under RCP8.5 in
29 West Africa (Diallo et al., 2016; Dosio, 2016; Sylla et al., 2016; Abiodun et al., 2017; Akinsanola and Zhou,
30 2018; Dosio et al., 2019) and is projected to increase (medium confidence) in central Africa (Fotso-Nguemo
31 et al., 2018, 2019; Sonkoué et al., 2019) and eastern Africa (Thiery et al., 2016; Ongoma et al., 2018a). In
32 northeast and central east Africa, extreme precipitation intensity is projected to increase across CMIP5,
33 CMIP6 and CORDEX-CORE (high confidence) in most areas annually (Coppola et al., 2021a), but the
34 trends differ from season to season in all future scenarios (Dosio et al., 2019). In northern Africa, there is low
35 confidence in the projected changes in heavy precipitation, either due to a lack of agreement among studies
36 on the sign of changes (Sillmann et al., 2013a; Giorgi et al., 2014) or due to insufficient evidence.
37
38 In Asia (Table 11.8), extreme precipitation will likely increase at global warming levels of 2°C and below,
39 but very likely increase at higher warming levels for the region as whole. The CMIP6 multi-model median
40 projects an increase in the 10- and 50-yr return values of Rx1day and Rx5day over more than 95% of
41 regions, even at the 2°C warming level, with larger increases at higher warming levels, independent of
42 emission scenarios (Li et al., 2020, also Figure 11.7). CMIP5 models produced similar projections. Both
43 heavy rainfall and rainfall intensity are projected to increase (Endo et al., 2017; Guo et al., 2016, 2018; Han
44 et al., 2018; Kim et al., 2018; Xu et al., 2016; Zhou et al., 2014). A half-degree difference in warming
45 between the 1.5°C and 2.0°C warming levels can result in a detectable increase in extreme precipitation over
46 the region (Li et al., 2020), in the Asian-Australian monsoon region (Chevuturi et al., 2018), and over South
47 Asia and China (Lee et al., 2018b; Li et al., 2018f). While there are regional differences, extreme
48 precipitation is projected to increase in almost all sub-regions, though there can be spatial heterogeneity
49 within sub-regions, such as in India (Shashikanth et al., 2018) and Southeast Asia (Ohba and Sugimoto,
50 2019). In East and Southeast Asia, there is high confidence that extreme precipitation is projected to intensify
51 (Guo et al., 2018; Li et al., 2018a; Seo et al., 2014; Sui et al., 2018; Wang et al., 2017b, 2017c; Xu et al.,
52 2016; Zhou et al., 2014, Nayak et al., 2017; Mandapaka and Lo, 2018; Raghavan et al., 2018; Tangang et al.,
53 2018; Supari et al., 2020). Extreme daily precipitation is also projected to increase in South Asia
54 (Shashikanth et al., 2018; Han et al., 2018; Xu et al., 2017). The extreme precipitation indices, including
55 Rx5day, R95p, and days of heavy precipitation (i.e., R10mm), are all projected to increase under the RCP4.5
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1 and RCP8.5 scenarios in central and northern Asia (Xu et al., 2017; Han et al., 2018). A general wetting
2 across the whole Tibetan Plateau and the Himalaya is projected, with increases in heavy precipitation in the
3 21st century (Zhou et al., 2014; Zhang et al., 2015c; Gao et al., 2018; Palazzi et al., 2013; Rajbhandari et al.,
4 2015; Wu et al., 2017; Paltan et al., 2018). Agreement in projected changes by different models is low in
5 regions of complex topography such as Hindu-Kush-Himalaya (Wester et al., 2019), but CMIP5, CMIP6 and
6 CORDEX-CORE simulations consistently project an increase in heavy precipitation in higher latitude areas
7 (WSB, ESB, RFE) (Coppola et al., 2021a) (high confidence).
8
9 In Australasia (Table 11.11), most CMIP5 models project an increase in Rx1day under RCP4.5 and RCP8.5
10 scenarios for the late 21st century (CSIRO and BOM, 2015; Alexander and Arblaster, 2017; Grose et al.,
11 2020) and the CMIP6 multi-model median projects an increase in the 10- and 50-yr return values of Rx1day
12 and Rx5day at a rate between 5-6% per degree celsius of near-surface global mean warming (Li et al., 2020,
13 also Figure 11.7). Yet, there is large uncertainty in the increase because projected changes in dynamic
14 processes lead to a decrease in Rx1day that can offsets the thermodynamic increase over a large portion of
15 the region (Pfahl et al., 2017, see also Box 11.1 Figure 1). Projected changes in moderate extreme
16 precipitation (the 99th percentile of daily precipitation) by RCMs under RCP8.5 for 2070-2099 are mixed,
17 with more regions showing decreases than increases (Evans et al., 2020). It is likely that daily rainfall
18 extremes such as Rx1day will increase at the continental scale for global warming levels at or above 3°C,
19 daily rainfall extremes are projected to increase at the 2.0°C global warming level (medium confidence), and
20 there is low confidence in changes at the 1.5°C. Projected changes show important regional differences with
21 very likely increases over NAU (Alexander and Arblaster, 2017; Herold et al., 2018; Grose et al., 2020) and
22 NZ (MfE, 2018) where projected dynamic contributions are small (Pfahl et al., 2017), see also Box 11.1
23 Figure 1) and medium confidence on increases over central, eastern, and southern Australia where dynamic
24 contributions are substantial and can affect local phenomena (CSIRO and BOM, 2015; Pepler et al., 2016;
25 Bell et al., 2019; Dowdy et al., 2019).
26
27 In Central and South America (Table 11.14), extreme precipitation will likely increase at global warming
28 levels of 2°C and below, but very likely increase at higher warming levels for the region as whole. A larger
29 increase in global surface temperature leads to a larger increase in extreme precipitation, independent of
30 emission scenarios (Li et al., 2020a). But there are regional differences in the projection and projected
31 changes for more moderate extreme precipitation are also more uncertain. Extreme precipitation, represented
32 by the R50mm and R90p extreme indices, is projected to increase on the eastern coast of SCA, but to decrease
33 along the Pacific coasts of El Salvador and Guatemala (Imbach et al., 2018). Chou et al. (2014) and Giorgi et
34 al. (2014) projected an increase in extreme precipitation over southeastern South America and the Amazon.
35 Projected changes in moderate extreme precipitation represented by the 99th percentile of daily precipitation
36 by different models under different emission scenarios, even at high warming levels, are mixed, with
37 increases projected for all regions by the CORDEX-CORE and CMIP5 simulations, but increases for some
38 regions and decreases for other regions by CMIP6 simulations (Coppola et al., 2021a). Extreme precipitation
39 is projected to increase in the La Plata basin (Cavalcanti et al., 2015; Carril et al., 2016). Taylor et al. (2018)
40 projected a decrease in days with intense rainfall in the Caribbean under 2°C global warming by the 2050s
41 under RCP4.5 relative to 1971-2000.
42
43 In Europe (Table 11.17), extreme precipitation will likely increase at global warming levels of 2°C and
44 below, but very likely increase for higher warming levels for the region as whole. The CMIP6 multi-model
45 median projects an increase in the 10- and 50-yr return values of Rx1day and Rx5day over a majority of the
46 region at the 2°C global warming level, with more than 95% of the region showing an increase at higher
47 warming levels (Li et al., 2020, also Figure 11.7). The most intense precipitation events observed today in
48 Europe are projected to almost double in occurrence for each degree celsius of further global warming
49 (Myhre et al., 2019). Extreme precipitation is projected to increase in both boreal winter and summer over
50 Europe (Madsen et al., 2014; OB et al., 2015; Nissen and Ulbrich, 2017). There are regional differences,
51 with decreases or no change for the southern part of Europe, such as the southern Mediterranean (Lionello
52 and Scarascia, 2020; Tramblay and Somot, 2018; Coppola et al., 2020), uncertain changes over central
53 Europe (Argüeso et al., 2012; Croitoru et al., 2013; Rajczak et al., 2013; Casanueva et al., 2014; Patarčić et
54 al., 2014; Paxian et al., 2014; Roth et al., 2014; Fischer and Knutti, 2015; Monjo et al., 2016) and a strong
55 increase in the remaining parts, including the Alps region (Gobiet et al., 2014; Donnelly et al., 2017),
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1 particularly in winter (Fischer et al., 2015), and northern Europe. In a 3°C warmer world, there will be a
2 robust increase in extreme rainfall over 80% of land areas in northern Europe (Madsen et al., 2014; Donnelly
3 et al., 2017; Cardell et al., 2020).
4
5 In North America (Table 11.20), the intensity and frequency of extreme precipitation will likely increase at
6 the global warming levels of 2°C and below and very likely increase at higher warming levels. An increase is
7 projected by CMIP6 model simulations (Li et al., 2020) and by previous model generations (Easterling et al.,
8 2017; Wu, 2015; Zhang et al. 2018f; Innocenti et al., 2019b), as well as by RCMs (Coppola et al., 2020).
9 Projections of extreme precipitation over the southern portion of the continent and over Mexico in particular
10 are more uncertain, with decreases possible (Alexandru, 2018; Sillmann et al., 2013b; Coppola et al., 2020).
11
12
13 [START FIGURE 11.16 HERE]
14
15 Figure 11.16:Projected changes in annual maximum daily precipitation at (a) 1.5°C, (b) 2°C, and (c) 4°C of global
16 warming compared to the 1851-1900 baseline. Results are based on simulations from the CMIP6 multi-
17 model ensemble under the SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The
18 numbers on the top right indicate the number of simulations included. Uncertainty is represented using
19 the simple approach: no overlay indicates regions with high model agreement, where ≥80% of models
20 agree on sign of change; diagonal lines indicate regions with low model agreement, where <80% of
21 models agree on sign of change. For more information on the simple approach, please refer to the Cross-
22 Chapter Box Atlas 1. For details on the methods see Supplementary Material 11.SM.2. Further details on
23 data sources and processing are available in the chapter data table (Table 11.SM.9).
24
25
26 [END FIGURE 11.16 HERE]
27
28
29 In summary, heavy precipitation will generally become more frequent and more intense with additional
30 global warming. At global warming levels of 4°C relative to the pre-industrial, very rare (e.g., 1 in 10 or
31 more years) heavy precipitation events would become more frequent and more intense than in the recent
32 past, on the global scale (virtually certain), and in all continents and AR6 regions: The increase in frequency
33 and intensity is extremely likely for most continents and very likely for most AR6 regions. The likelihood is
34 lower at lower global warming levels and for less-rare heavy precipitation events. At the global scale, the
35 intensification of heavy precipitation will follow the rate of increase in the maximum amount of moisture
36 that the atmosphere can hold as it warms (high confidence), of about 7% per °C of global warming.The
37 increase in the frequency of heavy precipitation events will accelerate with more warming and will be higher
38 for rarer events (high confidence), with 10-year and 50-year events to be approximately double and triple,
39 respectively, at the 4°C warming level. Increases in the intensity of extreme precipitation events at regional
40 scales will depend on the amount of regional warming as well as changes in atmospheric circulation and
41 storm dynamics leading to regional differences in the rate of heavy precipitation changes (high confidence).
42
43
44 11.5 Floods
45
46 Floods are the inundation of normally dry land and are classified into types (e.g., pluvial floods, flash floods,
47 river floods, groundwater floods, surge floods, coastal floods) depending on the space and time scales and
48 the major factors and processes involved (Chapter 8, Section 8.2.3.2, Nied et al., 2014; Aerts et al., 2018).
49 Flooded area is difficult to measure or quantify and, for this reason, many of the existing studies on changes
50 in floods focus on streamflow. Thus, this section assesses changes in flow as a proxy for river floods, in
51 addition to some types of flash floods. Pluvial and urban floods, types of flash floods resulting from the
52 precipitation intensity exceeding the capacity of natural and artificial drainage systems, are directly linked to
53 extreme precipitation. Because of this link, changes in extreme precipitation are the main proxy for inferring
54 changes in pluvial and urban floods (see also Section 12.4, REF Chapter 12), assuming there is no additional
55 change in the surface condition. Changes in these types of floods are not assessed in this section, but can be
56 inferred from the assessment of changes in heavy precipitation in Section 11.4. Coastal floods due to extreme
57 sea levels and flood changes at regional scales are assessed in Chapter 12 (12.4).
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1
2
3 11.5.1 Mechanisms and drivers
4
5 Since AR5, the number of studies on understanding how floods may have changed and will change in the
6 future has substantially increased. Floods are a complex interplay of hydrology, climate, and human
7 management, and the relative importance of these factors is different for different flood types and regions.
8
9 In addition to the amount and intensity of precipitation, the main factors for river floods include antecedent
10 soil moisture (Paschalis et al., 2014; Berghuijs et al., 2016; Grillakis et al., 2016; Woldemeskel and Sharma,
11 2016) and snow water-equivalent in cold regions (Sikorska et al., 2015; Berghuijs et al., 2016). Other factors
12 are also important, including stream morphology (Borga et al., 2014; Slater et al., 2015), river and catchment
13 engineering (Pisaniello et al., 2012; Nakayama and Shankman, 2013; Kim and Sanders, 2016), land-use and
14 land-cover characteristics (Aich et al., 2016; Rogger et al., 2017) and changes (Knighton et al., 2019), and
15 feedbacks between climate, soil, snow, vegetation, etc. (Hall et al., 2014; Ortega et al., 2014; Berghuijs et al.,
16 2016; Buttle et al., 2016; Teufel et al., 2019). Water regulation and management have, in general, increased
17 resilience to flooding (Formetta and Feyen, 2019), masking effects of an increase in extreme precipitation on
18 flood probability in some regions, even though they do not eliminate very extreme floods (Vicente-Serrano
19 et al., 2017). This means that an increase in precipitation extremes may not always result in an increase in
20 river floods (Sharma et al., 2018; Do et al., 2020). Yet, as very extreme precipitation can become a dominant
21 factor for river floods, there can then be some correspondence in the changes in very extreme precipitation
22 and river floods (Ivancic and Shaw, 2015; Wasko and Sharma, 2017; Wasko and Nathan, 2019). This has
23 been observed in the western Mediterranean (Llasat et al., 2016), in China (Zhang et al., 2015a) and in the
24 US (Peterson et al., 2013a; Berghuijs et al., 2016; Slater and Villarini, 2016).
25
26 In regions with a seasonal snow cover, snowmelt is the main cause of extreme river flooding over large areas
27 (Pall et al., 2019). Extensive snowmelt combined with heavy and/or long-duration precipitation can cause
28 significant floods (Li et al., 2019b; Krug et al., 2020). Changes in floods in these regions can be uncertain
29 because of the compounding and competing effects of the responses of snow and rain to warming that affect
30 snowpack size: warming results in an increase in precipitation, but also a reduction in the time period of
31 snowfall accumulation (Teufel et al., 2019). An increase in atmospheric CO2 enhances water-use efficiency
32 by plants (Roderick et al., 2015; Milly and Dunne, 2016; Swann et al., 2016; Swann, 2018); this could
33 reduce evapotranspiration and contribute to the maintenance of soil moisture and streamflow levels under
34 enhanced atmospheric CO2 concentrations (Yang et al., 2019). This mechanism would suggest an increase in
35 the magnitude of some floods in the future (Kooperman et al., 2018). But this effect is uncertain as an
36 increase in leaf area index and vegetation coverage could also result in overall larger water consumption
37 (Mátyás and Sun, 2014; Mankin et al., 2019; Teuling et al., 2019), and there are also other CO2-related
38 mechanisms that come into play (Chapter 5, CC Box 5.1).
39
40 Various factors, such as extreme precipitation (Cho et al., 2016; Archer and Fowler, 2018), glacier lake
41 outbursts (Schneider et al., 2014; Schwanghart et al., 2016), or dam breaks (Biscarini et al., 2016) can cause
42 flash floods. Very intense rainfall, along with a high fraction of impervious surfaces can result in flash floods
43 in urban areas (Hettiarachchi et al., 2018). Because of this direct connection, changes in very intense
44 precipitation can translate to changes in urban flood potential (Rosenzweig et al., 2018), though there can be
45 a spectrum of urban flood responses to this flood potential (Smith et al., 2013), as many factors such as the
46 overland flow rate and the design of urban (Falconer et al., 2009) and storm water drainage systems
47 (Maksimović et al., 2009) can play an important role. Nevertheless, changes in extreme precipitation are the
48 main proxy for inferring changes in some types of flash floods , which are addressed in Chapter 12 (Section
49 12.4)), given the relation between extreme precipitation and pluvial floods, the very limited literature on
50 urban and pluvial floods (e.g., Skougaard Kaspersen et al., 2017), and limitations of existing methodologies
51 for assessing changes in floods (Archer et al., 2016).
52
53 In summary, there is not always a one-to-one correspondence between an extreme precipitation event and a
54 flood event, or between changes in extreme precipitation and changes in floods, because floods are affected
55 by many factors in addition to heavy precipitation (high confidence). Changes in extreme precipitation may
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1 be used as a proxy to infer changes in some types of flash floods that are more directly related to extreme
2 precipitation (high confidence).
3
4
5 11.5.2 Observed trends
6
7 The SREX (Seneviratne et al., 2012) assessed low confidence for observed changes in the magnitude or
8 frequency of floods at the global scale. This assessment was confirmed by the AR5 report (Hartmann et al.,
9 2013). The SR15 (Hoegh-Guldberg et al., 2018) found increases in flood frequency and extreme streamflow
10 in some regions, but decreases in other regions. While the number of studies on flood trends has increased
11 since the AR5 report, and there were also new analyses after the release of SR15 (Berghuijs et al., 2017;
12 Blöschl et al., 2019; Gudmundsson et al., 2019), hydrological literature on observed flood changes is
13 heterogeneous, focusing at regional and sub-regional basin scales, making it difficult to synthesise at the
14 global and sometimes regional scales. The vast majority of studies focus on river floods using streamflow as
15 a proxy, with limited attention to urban floods. Streamflow measurements are not evenly distributed over
16 space, with gaps in spatial coverage, and their coverage in many regions of Africa, South America, and parts
17 of Asia is poor (e.g. Do et al., 2017), leading to difficulties in detecting long-term changes in floods (Slater
18 and Villarini, 2017). See also Chapter 8, Section 8.3.1.5.
19
20 Peak flow trends are characterized by high regional variability and lack overall statistical significance of a
21 decrease or an increase over the globe as a whole. Of more than 3500 streamflow stations in the US, central
22 and northern Europe, Africa, Brazil, and Australia, 7.1% stations showed a significant increase and 11.9%
23 stations showed a significant decrease in annual maximum peak flow during 1961-2005 (Do et al., 2017).
24 This is in direct contrast to the global and continental scale intensification of short-duration extreme
25 precipitation (11.4.2). There may be some consistency over large regions (see Gudmundsson et al., 2019), in
26 high streamflows (> 90th percentile), including a decrease in some regions (e.g., in the Mediterranean) and an
27 increase in others (e.g., northern Asia), but gauge coverage is often limited. On a continental scale, a
28 decrease seems to dominate in Africa (Tramblay et al., 2020) and Australia (Ishak et al., 2013; Wasko and
29 Nathan, 2019), an increase in the Amazon (Barichivich et al., 2018), and trends are spatially variable in other
30 continents (Do et al., 2017; Hodgkins et al., 2017; Bai et al., 2016; Zhang et al., 2015b). In Europe, flow
31 trends have large spatial differences (Hall et al., 2014; Mediero et al., 2015; Kundzewicz et al., 2018;
32 Mangini et al., 2018), but there appears to be a pattern of increase in northwestern Europe and a decrease in
33 southern and eastern Europe in annual peak flow during 1960-2000 (Blöschl et al., 2019). In North America,
34 peak flow has increased in the northeast US and decreased in the southwest US (Peterson et al., 2013a;
35 Armstrong et al., 2014; Mallakpour and Villarini, 2015; Archfield et al., 2016; Burn and Whitfield, 2016;
36 Wehner et al., 2017; Neri et al., 2019). There are important changes in the seasonality of peak flows in
37 regions where snowmelt dominates, such as northern North America (Burn and Whitfield, 2016; Dudley et
38 al., 2017) and northern Europe (Blöschl et al., 2017), corresponding to strong winter and spring warming.
39
40 In summary, the seasonality of floods has changed in cold regions where snowmelt dominates the flow
41 regime in response to warming (high confidence). Confidence about peak flow trends over past decades on
42 the global scale is low, but there are regions experiencing increases, including parts of Asia, southern South
43 America, the northeast USA, northwestern Europe, and the Amazon, and regions experiencing decreases,
44 including parts of the Mediterranean, Australia, Africa, and the southwestern USA.
45
46
47 11.5.3 Model evaluation
48
49 Hydrological models used to simulate floods are structurally diverse (Dankers et al., 2014; Mateo et al.,
50 2017; Şen, 2018), often requiring extensive calibration since sub-grid processes and land-surface properties
51 need to be parameterized, irrespective of the spatial resolutions (Döll et al., 2016; Krysanova et al., 2017).
52 The data that are used to drive and calibrate the models are usually of coarse resolution, necessitating the use
53 of a wide variety of downscaling techniques (Muerth et al., 2013). This adds uncertainty not only to the
54 models, but also to the reliability of the calibrations. The quality of the flood simulations also depends on the
55 spatial scale, as flood processes are different for catchments of different sizes. It is more difficult to replicate
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1 flood processes for large basins, as water management and water use are often more complex for these
2 basins.
3
4 Studies that use different regional hydrological models show large spread in flood simulations (Dankers et
5 al., 2014; Roudier et al., 2016; Trigg et al., 2016; Krysanova et al., 2017). Regional models reproduce
6 moderate and high flows (0.02 – 0.1 flow annual exceedance probabilities) reasonably well, but there are
7 large biases for the most extreme flows (0 - 0.02 annual flow exceedance probability), independent of the
8 climatic and physiographic characteristics of the basins (Huang et al., 2017)). Global-scale hydrological
9 models have even more challenges, as they struggle to reproduce the magnitude of the flood hazard (Trigg et
10 al., 2016). Additionally, the ensemble mean of multiple models does not perform better than individual
11 models (Zaherpour et al., 2018).
12
13 The use of hydrological models for assessing changes in floods, especially for future projections, adds
14 another dimension of uncertainty on top of uncertainty in the driving climate projections, including emission
15 scenarios, and uncertainty in the driving climate models (both RCMs and GCMs) (Arnell and Gosling, 2016;
16 Hundecha et al., 2016; Krysanova et al., 2017). The differences in hydrological models (Roudier et al., 2016;
17 Thober et al., 2018), as well as post-processing of climate model output for the hydrological models (Muerth
18 et al., 2013; Maier et al., 2018), both add to uncertainty for flood projections.
19
20 In summary, there is medium confidence that simulations for the most extreme flows by regional
21 hydrological models can have large biases. Global-scale hydrological models still struggle with reproducing
22 the magnitude of floods. Projections of future floods are hampered by these difficulties and cascading
23 uncertainties, including uncertainties in emission scenarios and the climate models that generate inputs.
24
25
26 11.5.4 Attribution
27
28 There are very few studies focused on the attribution of long-term changes in floods, but there are studies on
29 changes in flood events. Most of the studies focus on flash floods and urban floods, which are closely related
30 to intense precipitation events (Hannaford, 2015). In other cases, event attribution focused on runoff using
31 hydrological models, and examples include river basins in the UK (Schaller et al., 2016; Kay et al., 2018)
32 (See Section 11.4.4), the Okavango river in Africa (Wolski et al., 2014), and the Brahmaputra in Bangladesh
33 (Philip et al., 2019). Findings about anthropogenic influences vary between different regions and basins. For
34 some flood events, the probability of high floods in the current climate is lower than in a climate without an
35 anthropogenic influence (Wolski et al., 2014), while in other cases anthropogenic influence leads to more
36 intense floods (Cho et al., 2016; Pall et al., 2017; van der Wiel et al., 2017; Philip et al., 2018a; Teufel et al.,
37 2019). Factors such as land cover change and river management can also increase the probability of high
38 floods (Ji et al., 2020). These, along with model uncertainties and the lack of studies overall, suggest a low
39 confidence in general statements to attribute changes in flood events to anthropogenic climate change. Some
40 individual regions have been well studied, which allows for high confidence in the attribution of increased
41 flooding in these cases (Section 11.9 table). For example, flooding in the UK following increased winter
42 precipitation (Schaller et al., 2016; Kay et al., 2018) can be attributed to anthropogenic climate change
43 (Schaller et al., 2016; Vautard et al., 2016; Yiou et al., 2017; Otto et al., 2018b).
44
45 Attributing changes in heavy precipitation to anthropogenic activities (Section 11.4.4) cannot be readily
46 translated to attributing changes in floods to human activities, because precipitation is only one of the
47 multiple factors, albeit an important one, that affect floods. For example, Teufel et al. (2017) showed that
48 while human influence increased the odds of the flood-producing rainfall for the 2013 Alberta flood in
49 Canada, it was not detected to have influenced the probability of the flood itself. Schaller et al. (2016)
50 showed human influence on the increase in the probability of heavy precipitation translated linearly into an
51 increase in the resulting river flow of the Thames in winter 2014, but its contribution to the inundation was
52 inconclusive.
53
54 Gudmundsson et al. (2021) compared the spatial pattern of the observed regional trends in high river flows
55 (> 90th percentile) over 1971-2010 with those simulated by global hydrological models driven by outputs of
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1 climate models under all historical forcing and with pre-industrial climate model simulations. They found
2 complex spatial patterns of extreme river flow trends. They also found the observed spatial patterns of trends
3 can be reproduced only if anthropogenic climate change is considered and that simulated effects of water and
4 land management cannot reproduce the observed spatial pattern of trends. As there is only one study and
5 multiple caveats, including relatively poor observational data coverage, there is low confidence about human
6 influence on the changes in high river flows on the global scale.
7
8 In summary there is low confidence in the human influence on the changes in high river flows on the global
9 scale. Confidence is in general low in attributing changes in the probability or magnitude of flood events to
10 human influence because of a limited number of studies and differences in the results of these studies, and
11 large modelling uncertainties.
12
13
14 11.5.5 Future projections
15
16 The SREX report (Chapter 3, Seneviratne et al., 2012) stressed the low availability of studies on flood
17 projections under different emission scenarios and concluded there was low confidence in projections of
18 flood events given the complexity of the mechanisms driving floods at the regional scale. The AR5 WGII
19 report (Chapter 3, Jimenez Cisneros et al., 2014) assessed with medium confidence the pattern of future flood
20 changes, including flood hazards increasing over about half of the globe (parts of southern and Southeast
21 Asia, tropical Africa, northeast Eurasia, and South America) and flood hazards decreasing in other parts of
22 the world, despite uncertainties in GCMs and their coupling to hydrological models. SR15 (Chapter 3,
23 Hoegh-Guldberg et al., 2018) assessed with medium confidence that global warming of 2°C would lead to an
24 expansion of the fraction of global area affected by flood hazards, compared to conditions at 1.5°C of global
25 warming, as a consequence of changes in heavy precipitation.
26
27 The majority of new studies that produce future flood projections based on hydrological models do not
28 typically consider aspects that are also important to actual flood severity or damages, such as flood
29 prevention measures (Neumann et al., 2015; Şen, 2018), flood control policies (Barraqué, 2017), and future
30 changes in land cover (see also Chapter 8, Section 8.4.1.5). At the global scale, Alfieri et al. (2017a) used
31 downscaled projections from seven GCMs as input to drive a hydrodynamic model. They found successive
32 increases in the frequency of high floods in all continents except Europe, associated with increasing levels of
33 global warming (1.5°C, 2°C, 4°C). These results are supported by Paltan et al. (2018), who applied a
34 simplified runoff aggregation model forced by outputs from four GCMs. Huang et al. (2018b) used three
35 hydrological models forced with bias-adjusted outputs from four GCMs to produce projections for four river
36 basins including the Rhine, Upper Mississippi, Upper Yellow, and Upper Niger under 1.5ºC, 2ºC, and 3°C
37 global warming. This study found diverse projections for different basins, including a shift towards earlier
38 flooding for the Rhine and the Upper Mississippi, a substantial increase in flood frequency in the Rhine only
39 under the 1.5ºC and 2°C scenarios, and a decrease in flood frequency in the Upper Mississippi under all
40 scenarios.
41
42 At the continental and regional scales, the projected changes in floods are uneven in different parts of the
43 world, but there is a larger fraction of regions with an increase than with a decrease over the 21st century
44 (Hirabayashi et al., 2013; Dankers et al., 2014; Arnell and Gosling, 2016; Döll et al., 2018). These results
45 suggest medium confidence in flood trends at the global scale, but low confidence in projected regional
46 changes. Increases in flood frequency or magnitude are identified for southeastern and northern Asia and
47 India (high agreement across studies), eastern and tropical Africa, and the high latitudes of North America
48 (medium agreement), while decreasing frequency or magnitude is found for central and eastern Europe and
49 the Mediterranean (high confidence), and parts of South America, southern and central North America, and
50 southwest Africa (Hirabayashi et al., 2013; Dankers et al., 2014; Arnell and Gosling, 2016; Döll et al., 2018).
51 Over South America, most studies based on global and regional hydrological models show an increase in the
52 magnitude and frequency of high flows in the western Amazon (Sorribas et al., 2016; Langerwisch et al.,
53 2013; Guimberteau et al., 2013; Zulkafli et al., 2016) and the Andes (Hirabayashi et al., 2013; Bozkurt et al.,
54 2018). Chapter 12, Section 12.4, provides a detailed assessment of regional flood projections.
55
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1 In summary, global hydrological models project a larger fraction of land areas to be affected by an increase
2 in river floods than by a decrease in river floods (medium confidence). There is medium confidence that river
3 floods will increase in the western Amazon, the Andes, and southeastern and northern Asia. Regional
4 changes in river floods are more uncertain than changes in pluvial floods because complex hydrological
5 processes and forcings, including land cover change and human water management, are involved.
6
7
8 11.6 Droughts
9
10 Droughts refer to periods of time with substantially below-average moisture conditions, usually covering
11 large areas, during which limitations in water availability result in negative impacts for various components
12 of natural systems and economic sectors (Wilhite and Pulwarty, 2017; Ault, 2020). Depending on the
13 variables used to characterize it and the systems or sectors being impacted, drought may be classified in
14 different types (Figure 8.6; Table 11.A.1) such as meteorological (precipitation deficits), agricultural (e.g.,
15 crop yield reductions or failure, often related to soil moisture deficits), ecological (related to plant water
16 stress that causes e.g., tree mortality), or hydrological droughts (e.g., water shortage in streams or storages
17 such as reservoirs, lakes, lagoons, and groundwater) (See Annex VII: Glossary). The distinction of drought
18 types is not absolute as drought can affect different sub-domains of the Earth system concomitantly, but
19 sometimes also asynchronously, including propagation from one drought type to another (Brunner and
20 Tallaksen, 2019). Because of this, drought cannot be characterized using a single universal definition (Lloyd-
21 Hughes, 2014) or directly measured based on a single variable (SREX Chapter 3; Wilhite and Pulwarty,
22 2017). Drought can happen on a wide range of timescales - from "flash droughts" on a scale of weeks, and
23 characterized by a sudden onset and rapid intensification of drought conditions (Hunt et al., 2014; Otkin et
24 al., 2018; Pendergrass et al., 2020) to multi-year or decadal rainfall deficits (sometimes termed
25 “megadroughts”; Annex VII: Glossary) (Ault et al., 2014; Cook et al., 2016b; Garreaud et al., 2017).
26 Droughts are often analysed using indices that are measures of drought severity, duration and frequency
27 (Table 11.A.1; Chapter 8, Sections 8.3.1.6, 8.4.1.6, Chapter 12, Sections 12.3.2.6 and 12.3.2.7). There are
28 many drought indices published in the scientific literature, as also highlighted in the IPCC SREX report
29 (SREX Chapter 3). These can range from anomalies in single variables (e.g., precipitation, soil moisture,
30 runoff, evapotranspiration) to indices combining different atmospheric variables.
31
32 This assessment is focused on changes in physical conditions and metrics of direct relevance to droughts
33 (Table 11.A.1): a) precipitation deficits, b) excess of atmospheric evaporative demand (AED), c) soil
34 moisture deficits, d) hydrological deficits, and e) atmospheric-based indices combining precipitation and
35 AED. In the regional tables (Section 11.9), the assessment is structured by drought types, addressing i)
36 meteorological, ii) agricultural and ecological, and iii) hydrological droughts. Note that the latter two
37 assessments are directly informing the Chapter 12 assessment on projected regional changes in these climatic
38 impact-drivers (Chapter 12, Section 12.4). The text refers to AR6 regions acronyms (Section 11.9, see
39 Chapter 1, Section 1.4.5) when referring to changes in AR6 regions.
40
41
42 11.6.1 Mechanisms and drivers
43
44 Similar to many other extreme events, droughts occur as a combination of thermodynamic and dynamic
45 processes (Box 11.1). Thermodynamic processes contributing to drought, which are modified by greenhouse
46 gas forcing both at global and regional scales, are mostly related to heat and moisture exchanges and also
47 partly modulated by plant coverage and physiology. They affect, for instance, atmospheric humidity,
48 temperature, and radiation, which in turn affect precipitation and/or evapotranspiration in some regions and
49 time frames. On the other hand, dynamic processes are particularly important to explain drought variability
50 on different time scales, from a few weeks (flash droughts) to multiannual (megadroughts). There is low
51 confidence in the effects of greenhouse gas forcing on changes in atmospheric dynamic (Chapter 2, Section
52 2.4; Chapter 4, Section 4.3.3), and, hence, on associated changes in drought occurrence. Thermodynamic
53 processes are thus the main driver of drought changes in a warming climate (high confidence).
54
55
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1 11.6.1.1 Precipitation deficits
2
3 Lack of precipitation is generally the main factor controlling drought onset. There is high confidence that
4 atmospheric dynamics, which varies on interannual, decadal and longer time scales, is the dominant
5 contributor to variations in precipitation deficits in the majority of the world regions (Dai, 2013; Seager and
6 Hoerling, 2014; Miralles et al., 2014b; Burgman and Jang, 2015; Dong and Dai, 2015; Schubert et al., 2016;
7 Raymond et al., 2018; Baek et al., 2019; Drumond et al., 2019; Herrera-Estrada et al., 2019; Gimeno et al.,
8 2020; Mishra, 2020). Precipitation deficits are driven by dynamic mechanisms taking place on different
9 spatial scales, including synoptic processes –atmospheric rivers and extratropical cyclones, blocking and
10 ridges (Section 11.7; Sousa et al., 2017), dominant large-scale circulation patterns (Kingston et al., 2015),
11 and global ocean-atmosphere coupled patterns such as IPO, AMO and ENSO (Dai and Zhao, 2017). These
12 various mechanisms occur on different scales, are not independent, and substantially interact with one
13 another. Also regional moisture recycling and land-atmosphere feedbacks play an important role for some
14 precipitation anomalies (see below).
15
16 There is high confidence that land-atmosphere feedbacks play a substantial or dominant role in affecting
17 precipitation deficits in some regions (SREX, Chapter3; Gimeno et al., 2012; Guillod et al., 2015; Haslinger
18 et al., 2019; Herrera-Estrada et al., 2019; Koster et al., 2011; Santanello Jr. et al., 2018; Taylor et al., 2012;
19 Tuttle and Salvucci, 2016). The sign of the feedbacks can be either positive or negative, as well as local or
20 non-local (Taylor et al., 2012; Guillod et al., 2015; Tuttle and Salvucci, 2016). ESMs tend to underestimate
21 non-local negative soil moisture-precipitation feedbacks (Taylor et al., 2012) and also show high variations
22 in their representation in some regions (Berg et al., 2017a). Soil moisture-precipitation feedbacks contribute
23 to changes in precipitation in climate model projections in some regions, but ESMs display substantial
24 uncertainties in their representation, and there is thus only low confidence in these contributions (Berg et al.,
25 2017a; Vogel et al., 2017, 2018).
26
27
28 11.6.1.2 Atmospheric evaporative demand
29
30 Atmospheric evaporative demand (AED) quantifies the maximum amount of actual evapotranspiration (ET)
31 that can happen from land surfaces if they are not limited by water availability (Table 11.A.1). AED is
32 affected by both radiative and aerodynamic components. For this reason, the atmospheric dryness, often
33 quantified with the relative humidity or the vapor pressure deficit (VPD), is not equivalent to the AED, as
34 other variables are also highly relevant, including solar radiation and wind speed (Hobbins et al., 2012;
35 McVicar et al., 2012b; Sheffield et al., 2012). AED can be estimated using different methods (McMahon et
36 al., 2013). Methods solely based on air temperature (e.g. Hargreaves, Thornthwaite) usually overestimate it
37 in terms of magnitude and temporal trends (Sheffield et al., 2012), in particular in the context of substantial
38 background warming. Physically-based combination methods such as the Penman-Monteith equation are
39 more adequate and recommended since 1998 by the Food and Agriculture Oganization (Pereira et al., 2015).
40 For this reason, the assessment of this chapter, when considering atmospheric-based drought indices, only
41 includes AED estimates using the latter (see also Section 11.9). AED is generally higher than ET, since it
42 represents an upper bound for it. Hence, an AED increase does not necessarily lead to increased ET (Milly
43 and Dunne, 2016), in particular under drought conditions given soil moisture limitation (Bonan et al., 2014;
44 Berg et al., 2016; Konings et al., 2017; Stocker et al., 2018). In general, AED is highest in regions where ET
45 is lowest (e.g., desert areas), further illustrating the decoupling between the two variables under limited soil
46 moisture.
47
48 The influence of AED on drought depends on the drought type, background climate, the environmental
49 conditions and the moisture availability (Hobbins et al., 2016, 2017; Vicente-Serrano et al., 2020b). This
50 influence also includes effects not related to increased ET. Under low soil moisture conditions, increased
51 AED increases plant stress, enhancing the severity of agricultural and ecological droughts (Williams et al.,
52 2013; Allen et al., 2015; McDowell et al., 2016; Grossiord et al., 2020). Moreover, high VPD impacts
53 overall plant physiology; it affects the leaf and xylem safety margins, and decreases the sap velocity and
54 plant hydraulic conductance (Fontes et al., 2018). VPD also affects the plant metabolism of carbon and if
55 prolongued, it may cause plant mortality via carbon starvation (Breshears et al., 2013; Hartmann, 2015).
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1 Drought projections based exclusively on AED metrics overestimate changes in soil moisture and runoff
2 deficits. Nevertheless, AED also directly impacts hydrological drought, as ET from surface waters is not
3 limited (Wurbs and Ayala, 2014; Friedrich et al., 2018; Hogeboom et al., 2018; Xiao et al., 2018a), and this
4 effect increases under climate change projections (Wang et al., 2018c; Althoff et al., 2020). In addition, high
5 AED increases crop water consumptions in irrigated lands (García-Garizábal et al., 2014), contributing to
6 intensifying hydrological droughts downstream (Fazel et al., 2017; Vicente-Serrano et al., 2017).
7
8 On subseasonal to decadal scales, temporal variations in AED are strongly controlled by circulation
9 variability (Williams et al., 2014; Chai et al., 2018; Martens et al., 2018), but thermodynamic processes also
10 play a fundamental role and under human-induced climate change dominate the changes in AED.
11 Atmospheric warming due to increased atmospheric CO2 concentrations increases AED by means of
12 enhanced VPD in the absence of other influences (Scheff and Frierson, 2015). Indeed, because of the greater
13 warming over land than over oceans (Chapter 2, Section 2.3.1.1; Section 11.3), the saturation pressure of
14 water vapor increases more over land than over oceans; oceanic air masses advected over land thus contain
15 insufficient water vapour to keep pace with the greater increase in saturation vapour pressure over land
16 (Sherwood and Fu, 2014; Byrne and O’Gorman, 2018; Findell et al., 2019). Land-atmosphere feedbacks are
17 also important in affecting atmospheric moisture content and temperature, with resulting effects on relative
18 humidity and VPD (Berg et al., 2016; Haslinger et al., 2019; Zhou et al., 2019; Box 11.1).
19
20
21 11.6.1.3 Soil moisture deficits
22
23 Soil moisture shows an important correlation with precipitation variability (Khong et al., 2015; Seager et al.,
24 2019), but ET also plays a substantial role in further depleting moisture from soils, in particular in humid
25 regions during periods of precipitation deficits (Padrón et al., 2020; Teuling et al., 2013). In addition, soil
26 moisture plays a role in drought self-intensification under dry conditions in which ET is decreased and leads
27 to higher AED (Miralles et al., 2019), an effect that can also contribute to trigger “flash droughts” (Otkin et
28 al., 2016, 2018; DeAngelis et al., 2020; Pendergrass et al., 2020). If soil moisture becomes limited, ET is
29 reduced, which on one hand may decrease the rate of soil drying, but on the other hand can lead to further
30 atmospheric dryness through various feedback loops (Seneviratne et al., 2010; Miralles et al., 2014a, 2019;
31 Teuling, 2018; Vogel et al., 2018; Zhou et al., 2019b; Liu et al., 2020). The process is complex since
32 vegetation cover plays a role in modulating albedo and in providing access to deeper stores of water (both in
33 the soil and groundwater), and changes in land cover and in plant phenology may alter ET (Sterling et al.,
34 2013; Woodward et al., 2014; Frank et al., 2015; Döll et al., 2016; Ukkola et al., 2016; Trancoso et al., 2017;
35 Hao et al., 2019; Lian et al., 2020). Snow depth has strong and direct impacts on soil moisture in many
36 systems (Gergel et al., 2017; Williams et al., 2020).
37
38 Soil moisture directly affects plant water stress and ET. Soil moisture is the primary factor that controls
39 xylem hydraulic conductance, i.e. plant water uptake in plants (Sperry et al., 2016; Hayat et al., 2019; Chen
40 et al., 2020d). For this reason, soil moisture deficits are the main driver of xylem embolism, the primary
41 mechanism of plant mortality (Anderegg et al., 2012, 2016; Rowland et al., 2015). Also carbon assimilation
42 by plants strongly depend on soil moisture (Hartzell et al., 2017), with implications for carbon starvation and
43 plant dying if soil moisture deficits are prolongued (Sevanto et al., 2014). These mechanisms explain that
44 soil moisture deficits are usually more relevant than AED excess to explain gross primary production
45 anomalies and vegetation stress, mostly in sub-humid and semi-arid regions (Stocker et al. 2018; Liu et al.,
46 2020b). CO2 concentrations are shown to potentially decrease plant ET and increase plant water-use
47 efficiency, affecting soil moisture levels, although this effect interacts with other CO2 physiological and
48 radiative effects (Section 11.6.5.2; Chapter 5, CC Box 5.1), and has less relevance under low soil moisture
49 (Morgan et al., 2011; Xu et al., 2016b; Nackley et al., 2018; Dikšaitytė et al., 2019). ESMs represent both
50 surface (ca. 10cm) and total column soil moisture, whereby total soil moisture is of more direct relevance for
51 root water uptake, in particular by trees. There is evidence that surface soil moisture projections are
52 substantially drier than total soil moisture projections, and may thus overestimate drying of relevance for
53 most vegetation (Berg et al., 2017b).
54
55
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1 11.6.1.4 Hydrological deficits
2
3 Drivers of streamflow and surface water deficits are complex and strongly depend on the hydrological
4 system analysed (e.g., streamflows in the headwaters, medium course of the rivers, groundwater, highly
5 regulated hydrological basins). Soil hydrological processes, which control the propagation of meteorological
6 droughts throughout different parts of the hydrological cycle (Van Loon and Van Lanen, 2012), are spatially
7 and temporally complex (Herrera‐Estrada et al., 2017; Huang et al., 2017c) and difficult to quantify (Van
8 Lanen et al., 2016; Apurv et al., 2017; Caillouet et al., 2017; Konapala and Mishra, 2017; Hasan et al.,
9 2019). The physiographic characteristics of the basins also affect how droughts propagate throughout the
10 hydrological cycle (Van Loon and Van Lanen, 2012; Van Lanen et al., 2013; Van Loon, 2015; Konapala and
11 Mishra, 2020; Valiya Veettil and Mishra, 2020). In addition, the assessment of groundwater deficits is very
12 difficult given the complexity of processes that involve natural and human-driven feedbacks and interactions
13 with the climate system (Taylor et al., 2013). Streamflow and surface water deficits are affected by land
14 cover, groundwater and soil characteristics (Van Lanen et al., 2013; Van Loon and Laaha, 2015; Barker et
15 al., 2016; Tijdeman et al., 2018), as well as human activities (water management and demand, damming) and
16 land use changes (He et al., 2017; Jehanzaib et al., 2020; Van Loon et al., 2016; Veldkamp et al., 2017; Wu
17 et al., 2018; Xu et al., 2019b; Section 11.6.4.3). Finally, snow and glaciers are relevant for water resources in
18 some regions. For instance, warming affects snowpack levels (Dierauer et al., 2019; Huning and
19 AghaKouchak, 2020), as well as the timing of snow melt, thus potentially affecting the seasonality and
20 magnitude of low flows (Barnhart et al., 2016).
21
22
23 11.6.1.5 Atmospheric-based drought indices
24
25 Given difficulties of drought quantification and data constraints, atmospheric-based drought indices
26 combining both precipitation and AED have been developed, as they can be derived from meteorological
27 data that is available in most regions with few exceptions. These demand/supply indices are not intended to
28 be metrics of soil moisture, streamflow or vegetation water stress. Because of their reliance on precipitation
29 and AED, they are mostly related to the actual water balance in humid regions, in which ET is not limited by
30 soil moisture and tends towards AED. In water-limited regions and in dry periods everywhere, they
31 constitute an upper bound for overall water-balance deficits (e.g. of surface waters) but are also related
32 to conditions conducive to vegetation stress, particularly under soil moisture limitation (Section 11.6.1.2).
33
34 Although there are many atmospheric-based drought indices, two are assessed in this chapter: the Palmer
35 Drought Severity Index (PDSI) and the Standardized Precipitation Evapotranspiration Index (SPEI). The
36 PDSI has been widely used to monitor and quantify drought severity (Dai et al., 2018), but is affected by
37 some constraints (SREX Chapter 3; Mukherjee et al., 2018). Although the calculation of the PDSI is based
38 on a soil water budget, the PDSI is essentially a climate drought index that mostly responds to the
39 precipitation and the AED (van der Schrier et al., 2013; Vicente-Serrano et al., 2015; Dai et al., 2018). The
40 SPEI also combines precipitation and AED, being equally sensitive to these two variables (Vicente-Serrano
41 et al., 2015). The SPEI is more sensitive to AED than the PDSI (Cook et al., 2014a; Vicente-Serrano et al.,
42 2015), although under humid and normal precipitation conditions, the effects of AED on the SPEI are small
43 (Tomas-Burguera et al., 2020). Given the limitations associated with temperature-based AED estimates
44 (Section 11.6.1.2), only studies using the Penman-Monteith-based SPEI and PDSI (hereafter SPEI-PM and
45 PDSI-PM) are considered in this assessment and in the regional tables in Section 11.9.
46
47
48 11.6.1.6 Relation of assessed variables and metrics for changes in different drought types
49
50 This chapter assesses changes in meteorological drought, agricultural and ecological droughts, and
51 hydrological droughts. Precipitation-based indices are used for the estimation of changes in meteorological
52 droughts, such as the Standardized Precipitation Index (SPI) and the Consecutive Dry Days (CDD). Changes
53 in total soil moisture and soil moisture-based drought events are used for the estimation of changes in
54 agricultural and ecological droughts, complemented by changes in surface soil moisture, water-balance
55 estimates (precipitation minus ET), and SPEI-PM and PDSI-PM. For hydrological droughts, changes in low
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1 flows are assessed, sometimes complemented by changes in mean streamflow.
2
3 In summary, different drought types exist and they are associated with different impacts and respond
4 differently to increasing greenhouse gas concentrations. Precipitation deficits and changes in
5 evapotranspiration govern net water availability. A lack of sufficient soil moisture, sometimes amplified by
6 increased atmospheric evaporative demand, result in agricultural and ecological drought. Lack of runoff and
7 surface water result in hydrological drought. Drought events are both the result of dynamic and/or
8 thermodynamic processes, with thermodynamic processes being the main driver of drought changes under
9 human-induced climate change (high confidence).
10
11
12 11.6.2 Observed trends
13
14 Evidence on observed drought trends at the time of the SREX (Chapter 3) and AR5 (Chapter 2) was limited.
15 SREX concluded that “There is medium confidence that since the 1950s some regions of the world have
16 experienced a trend to more intense and longer droughts, in particular in southern Europe and West Africa,
17 but in some regions droughts have become less frequent, less intense, or shorter, for example, in central
18 North America and northwestern Australia”. The assessment at the time did not distinguish between different
19 drought types. This chapter includes numerous updates on observed drought trends, associated with
20 extensive new literature and longer datasets since the AR5.
21
22
23 11.6.2.1 Precipitation deficits
24
25 Strong precipitation deficits have been recorded in recent decades in the Amazon (2005, 2010), southwestern
26 China (2009-2010), southwestern North America (2011-2014), Australia (1997-2009), California (2014), the
27 middle East (2012-2016), Chile (2010-2015), the Great Horn of Africa (2011), among others (van Dijk et al.,
28 2013; Mann and Gleick, 2015; Rowell et al., 2015; Marengo and Espinoza, 2016; Dai and Zhao, 2017;
29 Garreaud et al., 2017, 2020; Marengo et al., 2017; Brito et al., 2018; Cook et al., 2018). Global studies
30 generally show no significant trends in SPI time series (Orlowsky and Seneviratne, 2013; Spinoni et al.,
31 2014), and in derived drought frequency and severity data (Spinoni et al., 2019), with very few regional
32 exceptions (Figure 11.17 and Section 11.9). Long-term decreases in precipitation are found in some AR6
33 regions in Africa (CAF, ESAF), and several regions in South America (NES, SAM, SWS, SSA) (Section
34 11.9). Evidence of precipitation-based drying trends is also found in Western Africa (WAF), consistent with
35 studies based on CDD trends (Chaney et al., 2014; Donat et al., 2014b; Barry et al., 2018; Dunn et al.,
36 2020)(Figure 11.17), however there is a partial recovery of the rainfall trends since the 1980s in this region
37 (Chapter 10, 10.4.2.1). Some AR6 regions show a decrease in meteorological drought, including NAU,
38 CAU, NEU and CNA (Section 11.9). Other regions do not show substantial trends in long-term
39 meteorological drought, or display mixed signals depending on the considered time frame and subregions,
40 such as in Southern Australia (SAU; Gallant et al., 2013; Delworth and Zeng, 2014; Alexander and
41 Arblaster, 2017; Spinoni et al., 2019; Dunn et al., 2020; Rauniyar and Power, 2020) and the Mediterranean
42 (MED; Camuffo et al., 2013; Gudmundsson and Seneviratne, 2016; Spinoni et al., 2017; Stagge et al., 2017;
43 Caloiero et al., 2018; Peña-Angulo et al., 2020) (see also Section 11.9 and Atlas 8.2).
44
45
46 11.6.2.2 Atmospheric evaporative demand
47
48 In several regions, AED increases have intensified recent drought events (Williams et al., 2014, 2020; Seager
49 et al., 2015b; Basara et al., 2019; García-Herrera et al., 2019), enhanced vegetation stress (Allen et al., 2015;
50 Sanginés de Cárcer et al., 2018; Yuan et al., 2019), or contributed to the depletion of soil moisture or runoff
51 through enhanced ET (Teuling et al., 2013; Padrón et al., 2020) (high confidence). Trends in pan evaporation
52 measurements and Penman-Monteith AED estimates provide an indication of possible trends in the influence
53 of AED on drought. Given the observed global temperature increases (Chapter 2; Section 2.3.1.1; Section
54 11.3) and dominant decrease in relative humidity over land areas (Simmons et al., 2010; Willett et al., 2014),
55 VPD has increased globally (Barkhordarian et al., 2019; Yuan et al., 2019). Pan evaporation has increased as
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1 a consequence of VPD changes in several AR6 regions such as East Asia (EAS; Li et al., 2013; Sun et al.,
2 2018; Yang et al., 2018a), West Central Europe (WCE; Mozny et al., 2020), MED; Azorin-Molina et al.,
3 2015) and Central and Southern Australia (CAU, SAU; Stephens et al., 2018). Nevertheless, there is an
4 important regional variability in observed trends, and in other AR6 regions pan evaporation has decreased
5 (e.g. in North Central America, NCA (Breña-Naranjo et al., 2016) and in the Tibetan Plateau, TIB (Zhang et
6 al., 2018a)). Physical models also show an important regional diversity, with an increase in New Zealand
7 (NZ; Salinger, 2013) and the Mediterranean (MED; Gocic and Trajkovic, 2014; Azorin-Molina et al., 2015;
8 Piticar et al., 2016), a decrease in SAS (Jhajharia et al., 2015), and strong spatial variability in North
9 America (Seager et al., 2015b). This variability is driven by the role of other meteorological variables
10 affecting AED. Changes in solar radiation as a consequence of solar dimming and brightening may affect
11 trends (Kambezidis et al., 2012; Sanchez-Lorenzo et al., 2015; Wang and Yang, 2014; Chapter 7, Section
12 7.2.2.2). Wind speed is also relevant (McVicar et al., 2012a), and studies suggest a reduction of the wind
13 speed in some regions (Zhang et al., 2019h) that could compensate the role of the VPD increase.
14 Nevertheless, the VPD trend seems to dominate the overall AED trends, compared to the effects of trends in
15 wind speed and solar radiation (Wang et al., 2012; Park Williams et al., 2017; Vicente-Serrano et al., 2020b).
16
17
18 11.6.2.3 Soil moisture deficits
19
20 There are limited long-term measurements of soil moisture from ground observations (Dorigo et al., 2011;
21 Qiu et al., 2016; Quiring et al., 2016), which impedes their use in the analysis of trends. Among the few
22 existing observational studies covering at least two decades, several studies have investigated trends in
23 ground soil moisture in East Asia (Section 11.9; (Chen and Sun, 2015b; Liu et al., 2015; Qiu et al., 2016)).
24 Alternatively, microwave-based satellite measurements of surface soil moisture have also been used to
25 analyse trends (Dorigo et al., 2012; Jia et al., 2018). Although there is regional evidence that microwave-
26 based soil moisture estimates can capture well drying trends in comparison with ground soil moisture
27 observations (Jia et al., 2018), there is only medium confidence in the derived trends, since satellite soil
28 moisture data are affected by inhomogeneities (Dorigo et al., 2015; Rodell et al., 2018; Preimesberger et al.,
29 2020). Furthermore, microwave-based satellites only sense surface soil moisture, which differs from root-
30 zone soil moisture (Berg et al., 2017b), although relationships can be derived between the two (Brocca et al.,
31 2011). Several studies have also analysed long-term soil moisture timeseries from observations-driven land-
32 surface or hydrological models, including land-based reanalysis products (Albergel et al., 2013; Jia et al.,
33 2018; Gu et al., 2019b; Markonis et al., 2021). Such models have also been used to assess changes in land
34 water availability, estimated as precipitation minus ET, which is equal to the sum of soil moisture and runoff
35 (Greve et al., 2014; Padrón et al., 2020).
36
37 Overall, evidence from global studies suggests that several land regions have been affected by increased soil
38 drying or water-balance in past decades, despite some spread among products (Albergel et al., 2013; Greve
39 et al., 2014; Gu et al., 2019b; Padrón et al., 2020). Drying has not only occurred in dry regions, but also in
40 humid regions (Greve et al., 2014). Some studies have specifically addressed changes in soil moisture at
41 regional scale (Section 11.9). For AR6 regions, several studies suggest an increase in the frequency and areal
42 extent of soil moisture deficits, with examples in East Asia (EAS; Cheng et al., 2015; Qin et al., 2015; Jia et
43 al., 2018), Western and Central Europe (WCE; Trnka et al., 2015b), and the Mediterranean (MED; Hanel et
44 al., 2018; Moravec et al., 2019; Markonis et al., 2021). Nonetheless, some analyses also show no long-term
45 trends in soil drying in some AR6 regions, e.g. in Eastern (ENA; Park Williams et al., 2017) and Central
46 North America (CNA; Seager et al., 2019), as well as in North-Eastern Africa (NEAF; Kew et al., 2021).
47 The soil moisture drying trends identified in both global and regional studies are generally related to
48 increases in ET (associated with higher AED) rather than decreases in precipitation, as identified on global
49 land for trends in water-balance in the dry season (Padrón et al., 2020), as well as for some regions (Teuling
50 et al., 2013; Cheng et al., 2015; Trnka et al., 2015a; Van Der Linden et al., 2019; Li et al., 2020c).
51
52 Evidence from observed or observations-derived trends in soil moisture and precipitation minus ET, are
53 combined with evidence from SPEI and PDSI-PM studies to derive regional assessments of changes in
54 agricultural and ecological droughts (Section 11.9). This assessment is summarized in Section 11.6.2.6.
55
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1
2 11.6.2.4 Hydrological deficits
3
4 There is evidence based on streamflow records of increased hydrological droughts in East Asia (Zhang et al.,
5 2018b) and southern Africa (Gudmundsson et al., 2019). In areas of Western and Central Europe and of
6 Northern Europe, there is no evidence of changes in the severity of hydrological droughts since 1950 based
7 on flow reconstructions (Caillouet et al., 2017; Barker et al., 2019) and observations (Vicente-Serrano et al.,
8 2019). In the Mediterranean region, there is high confidence in hydrological drought intensification (Giuntoli
9 et al., 2013; Gudmundsson et al., 2019; Lorenzo-Lacruz et al., 2013; Masseroni et al., 2020; Section 11.9). In
10 Southeastern South America there is a decrease in the severity of hydrological droughts (Rivera and Penalba,
11 2018). In North America, depending on the methods, datasets and study periods, there are differences
12 between studies that suggest an increase (Shukla et al., 2015; Udall and Overpeck, 2017) vs a decrease in
13 hydrological drought frequency (Mo and Lettenmaier, 2018), but in general there is strong spatial variability
14 (Poshtiri and Pal, 2016). Streamflow observation reference networks of near-natural catchments have also
15 been used to isolate the effect of climate trends on hydrological drought trends in a few regions, but these
16 show limited trends in Northern Europe and Western and Central Europe (Stahl et al., 2010; Bard et al.,
17 2015; Harrigan et al., 2018), North America (Dudley et al., 2020) and most of Australia with the exception
18 of Eastern and Southern Australia (Zhang et al., 2016c). Given the low availability of observations, there are
19 few studies analysing trends of drought severity in the groundwater. Nevertheless, some studies suggest a
20 noticeable response of groundwater droughts to climate variability (Lorenzo-Lacruz et al., 2017) and
21 increased drought frequency and severity associated with warming, probably as a consequence of enhanced
22 ET induced by higher AED (Maxwell and Condon, 2016). This is supported by studies in Northern Europe
23 (Bloomfield et al., 2019) and North America (Condon et al., 2020).
24
25
26 11.6.2.5 Atmospheric-based drought indices
27
28 Globally, trends in SPEI-PM and PDSI-PM suggest slightly higher increases of drought frequency and
29 severity in regions affected by drying over the last decades in comparison to the SPI (Dai and Zhao, 2017;
30 Spinoni et al., 2019; Song et al., 2020), mainly in regions of West and Southern Africa, the Mediterranean
31 and East Asia (Figure 11.17), which is consistent with observed soil moisture trends (Section 11.6.2.3).
32 These indices suggest that AED has contributed to increase the severity of agricultural and ecological
33 droughts compared to meteorological droughts (García-Herrera et al., 2019; Williams et al., 2020), reduce
34 soil moisture during the dry season (Padrón et al., 2020), increase plant water stress (Allen et al., 2015;
35 Grossiord et al., 2020; Solander et al., 2020) and trigger more severe forest fires (Abatzoglou and Williams,
36 2016; Turco et al., 2019; Nolan et al., 2020). A number of regional studies based on these drought indices
37 have also shown stronger drying trends in comparison to trends in precipitation-based indices in the
38 following AR6 regions (see also 11.9): NSA (Fu et al., 2013b; Marengo and Espinoza, 2016), SCA (Hidalgo
39 et al., 2017), WCA (Tabari and Aghajanloo, 2013; Sharafati et al., 2020), SAS (Niranjan Kumar et al.,
40 2013), NEAF (Zeleke et al., 2017), WSAF (Edossa et al., 2016), NWN and NEN (Bonsal et al., 2013), EAS
41 (Yu et al., 2014; Chen and Sun, 2015b; Li et al., 2020b; Liang et al., 2020; Wu et al., 2020b) and MED
42 (Kelley et al., 2015; Stagge et al., 2017; González-Hidalgo et al., 2018; Mathbout et al., 2018a).
43
44
45 [START FIGURE 11.17 HERE]
46
47 Figure 11.17:Observed linear trend for (a) consecutive dry days (CDD) during 1960-2018, (b) standardized
48 precipitation index (SPI) and (c) standardized precipitation-evapotranspiration index (SPEI) during 1951-
49 2016. CDD data are from the HadEx3 dataset (Dunn et al., 2020), trend calculation of CDD as in Figure
50 11.9 Drought severity is estimated using 12-month SPI (SPI-12) and 12-month SPEI (SPEI-12). SPI and
51 SPEI datasets are from Spinoni et al. (2019). The threshold to identify drought episodes was set at -1
52 SPI/SPEI units. Areas without sufficient data are shown in grey. No overlay indicates regions where the
53 trends are significant at p = 0.1 level. Crosses indicate regions where trends are not significant. For details
54 on the methods see Supplementary Material 11.SM.2. Further details on data sources and processing are
55 available in the chapter data table (Table 11.SM.9).
56
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1 [END FIGURE 11.17 HERE]
2
3
4 11.6.2.6 Synthesis for different drought types
5
6 Few AR6 regions show observed increases in meteorological drought (Section 11.9), mostly in Africa and
7 South America (NES: high confidence; WAF, CAF, ESAF, SAM, SWS, SSA, SAS: medium confidence); a
8 few others show a decrease (WSB, ESB, NAU, CAU, NEU, CNA: medium confidence). There are stronger
9 signals indicating observed increases in agricultural and ecological drought (Section 11.9), which highlights
10 the role of increased ET, driven by increased AED, for these trends (Sections 11.6.2.3, 11.6.2.5). Past
11 increases in agricultural and ecological droughts are found on all continents and several regions (WAF, CAF,
12 WSAF, ESAF, WCA, ECA, EAS, SAU, MED, WCE, NES: medium confidence), while decreases are found
13 only in one AR6 region (NAU: medium confidence). The more limited availability of datasets makes it more
14 difficult to assess historical trends in hydrological drought at regional scale (Section 11.9). Increasing (MED:
15 high confidence; WAF, EAS, SAU: medium confidence) and decreasing (NEU, SES: medium confidence)
16 trends in hydrological droughts have only been observed in a few regions.
17
18 In summary, there is high confidence that AED has increased on average on continents, contributing to
19 increased ET and resulting water stress during periods with precipitation deficits, in particular during dry
20 seasons. There is medium confidence in increases in precipitation deficits in a few regions of Africa and
21 South America. Based on multiple evidence, there is medium confidence that agricultural and ecological
22 droughts have increased in several regions on all continents (WAF, CAF, WSAF, ESAF, WCA, ECA, EAS,
23 SAU, MED, WCE, NES: medium confidence), while there is only medium confidence in decreases in one
24 AR6 region (NAU). More frequent hydrological droughts are found in fewer regions (MED: high
25 confidence; WAF, EAS, SAU: medium confidence).
26
27
28 11.6.3 Model evaluation
29
30 11.6.3.1 Precipitation deficits
31
32 ESMs generally show limited performance and large spread in identifying precipitation deficits and
33 associated long-term trends in comparison with observations (Nasrollahi et al., 2015). Meteorological
34 drought trends in the CMIP5 ensemble showed substantial disagreements compared with observations
35 (Orlowsky and Seneviratne, 2013; Knutson and Zeng, 2018) including a tendency to overestimate drying, in
36 particular in mid- to high latitudes (Knutson and Zeng, 2018). CMIP6 models display a better performance in
37 reproducing long-term precipitation trends or seasonal dynamics in some studies in southern South America
38 (Rivera and Arnould, 2020), East Asia (Xin et al., 2020), southern Asia (Gusain et al., 2020), and
39 southwestern Europe (Peña-Angulo et al., 2020b), but there is still too limited evidence to allow for an
40 assessment of possible differences in performance between CMIP5 and CMIP6. Furthermore, ESMs are
41 generally found to underestimate the severity of precipitation deficits and the dry day frequencies in
42 comparison to observations (Fantini et al., 2018; Ukkola et al., 2018). This is probably related to
43 shortcomings in the simulation of persistent weather events in the mid-latitudes (Chapter 10, Section
44 10.3.3.3). In addition, ESMs also show a tendency to underestimate precipitation-based drought persistence
45 at monthly to decadal time scales (Ault et al., 2014; Moon et al., 2018). The overall inter-model spread in
46 the projected frequency of precipitation deficits is also substantial (Touma et al., 2015; Zhao et al., 2016;
47 Engström and Keellings, 2018). Moreover, there are spatial differences in the spread, which is higher in the
48 regions where enhanced drought conditions are projected and under high-emission scenarios (Orlowsky and
49 Seneviratne, 2013). Nonetheless, some event attribution studies have concluded that droughts at regional
50 scales can be adequately simulated by some climate models (Schaller et al., 2016; Otto et al., 2018c).
51
52
53 11.6.3.2 Atmospheric evaporative demand
54
55 There is only limited evidence on the evaluation of AED in state-of-the-art ESMs, which is performed on
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1 externally computed AED based on model output (Scheff and Frierson, 2015; Liu and Sun, 2016, 2017). An
2 evaluation of average AED in 17 CMIP5 ESMs for 1981-1999 based on potential evaporation show that the
3 models’ spatial patterns resemble the observations, but that the magnitude of potential evaporation displays
4 strong divergence among models globally and regionally (Scheff and Frierson, 2015). The evaluation of
5 AED in 12 CMIP5 ESMs with pan evaporation observations in East Asia for 1961-2000 (Liu and Sun, 2016,
6 2017) show that the ESMs capture seasonal cycles well, but that regional AED averages are underestimated
7 due to biases in the meteorological variables controlling the aerodynamic and radiative components of AED.
8 CMIP5 ESMs also show a strong underestimation of atmospheric drying trends compared to reanalysis data
9 (Douville and Plazzotta, 2017).
10
11
12 11.6.3.3 Soil moisture deficits
13
14 The performance of climate models for representing soil moisture deficits shows more uncertainty than for
15 precipitation deficits since in addition to the uncertainties related to cloud and precipitation processes, there
16 is uncertainty related to the representation of complex soil hydrological and boundary-layer processes (Van
17 Den Hurk et al., 2011; Lu et al., 2019; Quintana-Seguí et al., 2020). A limitation is also the lack of
18 observations, and in particular soil moisture, in most regions (Section 11.6.2.3), and the paucity of land
19 surface property data to parameterize land surface models, in particular soil types, soil properties and depth
20 (Xia et al., 2015). The spatial resolution of models is an additional limitation since the representation of
21 some land-atmosphere feedbacks and topographic effects requires detailed resolution (Nicolai‐Shaw et al.,
22 2015; Van Der Linden et al., 2019). Beside climate models, also land surface and hydrological models are
23 used to derive historical and projected trends in soil moisture and related land water variables (Albergel et
24 al., 2013; Cheng et al., 2015; Gu et al., 2019b; Padrón et al., 2020; Markonis et al., 2021; Pokhrel et al.,
25 2021).
26
27 Overall, there are contrasting results on the performance of land surface models and climate models in
28 representing soil moisture. Some studies suggest that soil moisture anomalies are well captured by land
29 surface models driven with observation-based forcing (Dirmeyer et al., 2006; Albergel et al., 2013; Xia et al.,
30 2014; Balsamo et al., 2015; Reichle et al., 2017; Spennemann et al., 2020), but other studies report limited
31 agreement in the representation of interannual soil moisture variability (Stillman et al., 2016; Yuan and
32 Quiring, 2017; Ford and Quiring, 2019) and noticeable seasonal differences in model skill (Xia et al., 2014,
33 2015) in some regions. Models with good skill can nonetheless display biases in absolute soil moisture (Xia
34 et al., 2014; Gu et al., 2019a), but these are not necessarily of relevance for the simulation of surface water
35 fluxes and drought anomalies (Koster et al., 2009). There is also substantial intermodel spread (Albergel et
36 al. 2013), particularly for the root-zone soil moisture (Berg et al., 2017b).
37
38 Regarding the performance of regional and global climate models, an evaluation of an ensemble of RCM
39 simulations for Europe (Stegehuis et al., 2013) shows that these models display too strong drying in early
40 summer, resulting in an excessive decrease of latent heat fluxes, with potential implications for more severe
41 droughts in dry environments (Teuling, 2018; Van Der Linden et al., 2019). Compared with a range of
42 observational ET estimates, CMIP5 models show an overestimation of ET on annual scale, but an ET
43 underestimation in boreal summer in many North-Hemisphere mid-latitude regions, also suggesting a
44 tendency towards excessive soil drying (Mueller and Seneviratne, 2014), consistent with identified biases in
45 soil moisture-temperature coupling (Donat et al., 2018; Vogel et al., 2018; Selten et al., 2020). Land surface
46 models used in ESM display a bias in their representation of the sensitivity of interannual land carbon uptake
47 to soil moisture conditions, which appears related to a limited range of soil moisture variations compared to
48 observations (Humphrey et al., 2018).
49
50 For future projections, the spread of soil moisture outputs among different ESMs is more important than
51 internal variability and scenario uncertainty, and the bias is strongly related to the sign of the projected
52 change (Ukkola et al., 2018; Lu et al., 2019; Selten et al., 2020). CMIP5 ESMs that project more drying and
53 warming in mid-latitude regions show a substantial bias in soil moisture-temperature coupling (Donat et al.,
54 2018; Vogel et al., 2018). Although CMIP6 and CMIP5 simulations for soil moisture changes are overall
55 similar, some differences are found in projections in a few regions (Cook et al., 2020)(see also Section 11.9).
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1 There is still limited evidence to assess whether there are substantial differences in model performance in the
2 two ensembles, but improvements in modeling aspects relevant for soil moisture have been reported for
3 precipitation (11.6.3.2), and a better performance has been found in CMIP6 for the representation of long-
4 term trends in soil moisture in the continental USA (Yuan et al., 2021). Despite the mentioned model
5 limitations, the representation of soil moisture processes in ESMs uses physical and biological understanding
6 of the underlying processes, which can represent well the temporal anomalies associated with temporal
7 variability and trends in climate. In summary, there is medium confidence in the representation of soil
8 moisture deficits in ESMs and related land surface and hydrological models.
9
10
11 11.6.3.4 Hydrological deficits
12
13 Streamflow and groundwater are not directly simulated by ESMs, which only simulate runoff, but they are
14 generallyn represented in hydrological models (Prudhomme et al., 2014; Giuntoli et al., 2015), which are
15 typically driven in a stand-alone manner by observed or simulated climate forcing. The simulation of
16 hydrological deficits is much more problematic than the simulation of mean streamflow or peak flows
17 (Fundel et al., 2013; Stoelzle et al., 2013; Velázquez et al., 2013; Staudinger et al., 2015), since models tend
18 to be too responsive to the climate forcing and do not satisfactorily capture low flows (Tallaksen and Stahl,
19 2014). Simulations of hydrological drought metrics show uncertainties related to the contribution of both
20 GCMs and hydrological models (Bosshard et al., 2013; Giuntoli et al., 2015; Samaniego et al., 2017; Vetter
21 et al., 2017), but hydrological models forced by the same climate input data also show a large spread (Van
22 Huijgevoort et al., 2013; Ukkola et al., 2018). At the catchment scale, the hydrological model uncertainty is
23 higher than both GCM and downscaling uncertainty (Vidal et al., 2016), and the hydrological models show
24 issues in representing drought propagation throughout the hydrological cycle (Barella-Ortiz and Quintana
25 Seguí, 2019). A study on the evaluation of streamflow droughts in seven global (hydrological and land
26 surface) models compared with observations in near-natural catchments of Europe showed a substantial
27 spread among models, an overestimation of the number of drought events, and an underestimation of drought
28 duration and drought-affected area (Tallaksen and Stahl, 2014).
29
30
31 11.6.3.5 Atmospheric-based drought indices
32
33 A number of studies have analysed the ability of models to capture drought severity and trends based on
34 climatic drought indices. Given the limitations of ESMs in reproducing the dynamic of precipitation deficits
35 and AED (11.6.3.1, 11.6.3.2), atmospheric-based drought indices derived from ESM data for these two
36 variables are also affected by uncertainties and biases. A comparison of historical trends in PDSI-PM for
37 1950-2014 derived from CMIP3 and CMIP5 with respective estimates derived from observations (Dai and
38 Zhao, 2017) show a similar behaviour at global scale (long-term decrease), but low spatial agreement in the
39 trends except in a few regions (Mediterranean, South Asia, northwestern US). In future projections there is
40 an important spread in PDSI-PM and SPEI-PM among different models (Cook et al., 2014a).
41
42
43 11.6.3.6 Synthesis for different drought types
44
45 The performance of ESMs used to assessed changes in variables related to meteorological droughts,
46 agricultural and ecological droughts, and hydrological droughts, show the presence of biases and
47 uncertainties compared to observations, but there is medium confidence in their overall performance for
48 assessing drought projections given process understanding. Given the substantial inter-model spread
49 documented for all related variables, the consideration of multi-model projections increases the confidence
50 of model-based assessments, with only low confidence in assessments based on single models.
51
52 In summary, the evaluation of ESMs, land surface and hydrological models for the simulation of droughts is
53 complex, due to the regional scale of drought trends, their overall low signal-to-noise ratio, and the lack of
54 observations in several regions, in particular for soil moisture and streamflow. There is medium confidence
55 in the ability of ESMs to simulate trends and anomalies in precipitation deficits and AED, and also medium
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1 confidence in the ability of ESMs and hydrological models to simulate trends and anomalies in soil moisture
2 and streamflow deficits, on global and regional scales.
3
4
5 11.6.4 Detection and attribution, event attribution
6
7 11.6.4.1 Precipitation deficits
8
9 There are only two AR6 regions in which there is at least medium confidence that human-induced climate
10 change has contributed to changes in meteorological droughts (Section 11.9). In South-western South
11 America (SSW), there is medium confidence that human-induced climate change has contributed to an
12 increase in meteorological droughts (Boisier et al., 2016; Garreaud et al., 2020), while in Northern Europe
13 (NEU), there is medium confidence that it has contributed to a decrease in meteorological droughts
14 (Gudmundsson and Seneviratne, 2016) (Section 11.9). In other AR6 regions, there is inconclusive evidence
15 in the attribution of long-term trends, but a human contribution to single meteorological events or
16 subregional trends has been identified in some instances (Section 11.9; see also below). In the Mediterranean
17 (MED) region, some studies have identified a precipitation decline or increase in meteorological drought
18 probability for time frames since the early or mid 20th century and a possible human contribution to these
19 trends (Hoerling et al., 2012; Gudmundsson and Seneviratne, 2016; Knutson and Zeng, 2018), also on
20 subregional scale in Syria from 1930 to 2010 (Kelley et al., 2015). On the contrary, other studies have not
21 identified precipitation and meteorological drought trends in the region for the long-term (Camuffo et al.,
22 2013; Paulo et al., 2016; Vicente‐Serrano et al., 2021) and also from the mid 20th century (Norrant and
23 Douguédroit, 2006; Stagge et al., 2017). There is evidence of substantial internal variability in long-term
24 precipitation trends in the region (Section 11.6.2.1), which limits the attribution of human influence on
25 variability and trends of meteorological droughts from observational records (Kelley et al., 2012; Peña-
26 Angulo et al., 2020b). In addition, there are important subregional trends showing mixed signals (MedECC,
27 2020)(Section 11.9). The evidence thus leads to an assessment of low confidence in the attribution of
28 observed short-term changes in meteorological droughts in the region (Section 11.9). In North America, the
29 human influence on precipitation deficits is complex (Wehner et al., 2017), with low confidence in the
30 attribution of long-term changes in meteorological drought in AR6 regions (Lehner et al., 2018; Section
31 11.9). In Africa there is low confidence that human influence has contributed to the observed long-term
32 meteorological drought increase in Western Africa (Section 11.9; Chapter 10, Section 10.6.2). There is low
33 confidence in the attribution of the observed increasing trends in meteorological drought in Eastern Southern
34 Africa, but evidence that human-induced climate change has affected recent meteorological drought events
35 in the region (11.9).
36
37 Attribution studies for recent meteorological drought events are available for various regions. In Central and
38 Western Europe, a multi-method and multi-model attribution study on the 2015 Central European drought
39 did not find conclusive evidence for whether human-induced climate change was a driver of the rainfall
40 deficit, as the results depended on model and method used (Hauser et al., 2017). In the Mediterranean region,
41 a human contribution was found in the case of the 2014 meteorological drought in the southern Levant based
42 on a single-model study (Bergaoui et al., 2015). In Africa, there is some evidence of a contribution of human
43 emissions to single meteorological drought events, such as the 2015-2017 southern African drought (Funk et
44 al., 2018a; Yuan et al., 2018a; Pascale et al., 2020), and the three-year 2015-2017 drought in the western
45 Cape Town region of South Africa (Otto et al., 2018c). An attributable signal was not found in droughts that
46 occurred in different years with different spatial extents in the last decade in Northern and Southern East
47 Africa (Marthews et al., 2015; Uhe et al., 2017; Otto et al., 2018a; Philip et al., 2018b; Kew et al., 2021).
48 However, an attributable increase in 2011 long rain failure was identified (Lott et al., 2013). Further studies
49 have attributed some African meteorological drought events to large-scale modes of variability, such as the
50 strong 2015 El Niño (Philip et al., 2018; Box 11.4) and increased SSTs overall (Funk et al., 2015b, 2018b).
51 Natural variability was dominant in the California droughts of 2011/12-2013/14 (Seager et al., 2015a). In
52 Asia, no climate change signal was found in the record dry spell over Singapore-Malaysia in 2014 (Mcbride
53 et al., 2015) or the drought in central southwest Asia in 2013/2014 (Barlow and Hoell, 2015). Nevertheless,
54 the South East Asia drought of 2015 has been attributed to anthropogenic warming effects (Shiogama et al.,
55 2020). Recent droughts occurring in South America, specifically in the southern Amazon region in 2010
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1 (Shiogama et al., 2013) and in Northeast South America in 2014 (Otto et al., 2015) and 2016 (Martins et al.,
2 2018) were not attributed to anthropogenic climate change. Nevertheless, the central Chile drought between
3 2010 and 2018 has been suggested to be partly associated to global warming (Boisier et al., 2016; Garreaud
4 et al., 2020). The 2013 New Zealand meteorological drought was attributed to human influence by
5 Harrington et al. (2014, 2016) based on fully coupled CMIP5 models, but, no corresponding change in the
6 dry end of simulated precipitation from a stand-alone atmospheric model was found by Angélil et al. (2017).
7
8 Event attribution studies also highlight a complex interplay of anthropogenic and non-anthropogenic
9 climatological factors for some events. For example, anthropogenic warming contributed to the 2014 drought
10 in North Eastern-Africa by increasing east African and west Pacific temperatures, and increasing the gradient
11 between standardized western and central Pacific SSTs causing reduced rainfall (Funk et al., 2015b). As
12 different methodologies, models and data sources have been used for the attribution of precipitation deficits,
13 Angélil et al. (2017) reexamined several events using a single analytical approach and climate model and
14 observational datasets. Their results showed a disagreement in the original anthropogenic attribution in a
15 number of precipitation deficit events, which increased uncertainty in the attribution of meteorological
16 droughts events.
17
18
19 11.6.4.2 Soil moisture deficits
20
21 There is a growing number of studies on the detection and attribution of long-term changes in soil moisture
22 deficits. Mueller and Zhang (2016) concluded that anthropogenic forcing contributed significantly to an
23 increase in the land surface area affected by soil moisture deficits, which can be reproduced by CMIP5
24 models only if anthropogenic forcings are involved. A similar assessment was provided globally by Gu et al.
25 (2019b) also using CMIP5 models. Padrón et al. (2019) analyzed long-term reconstructed and CMIP5
26 simulated dry season water availability, defined as precipitation minus ET (i.e., equivalent to soil moisture
27 and runoff availability), and found that patterns of changes in dry-season deficits in the recent three last
28 decades can only be explained by anthropogenic forcing and are mostly related to changes in ET. Similarly
29 Williams et al. (2020) concluded human-induced climate change contributed to the strong soil moisture
30 deficits recorded in the last two decades in western North America through VPD increases associated with
31 higher air temperatures and lower air humidity. There are few studies analysing the attribution of particular
32 episodes of soil moisture deficits to anthropogenic influence. Nevertheless, the available modeling studies
33 coincide in supporting an anthropogenic attribution associated with more extreme temperatures, exacerbating
34 AED and increasing ET, and thus depleting soil moisture, as observed in southern Europe in 2017 (García-
35 Herrera et al., 2019) and in Australia in 2018 (Lewis et al., 2019b) and 2019 (van Oldenborgh et al., 2021),
36 the latter event having strong implications in the propagation of widespread mega-fires (Nolan et al., 2020).
37
38
39 11.6.4.3 Hydrological deficits
40
41 It is often difficult to separate the role of climate trends from changes in land use, water management and
42 demand for changes in hydrological deficits, especially on regional scale. However, a global study based on
43 a recent multi-model experiment with global hydrological models and covering several AR6 regions
44 suggests a dominant role of anthropogenic radiative forcing for trends in low, mean and high flows, while
45 simulated effects of water and land management do not suffice to reproduce the observed spatial pattern of
46 trends (Gudmundsson et al., 2021). Regional studies also suggest that climate trends have been dominant
47 compared to land use and human water management for explaining trends in hydrological droughts in some
48 regions, for instance in Ethiopia (Fenta et al., 2017), in China (Xie et al., 2015), and in North America for the
49 Missouri and Colorado basins, as well as in California (Shukla et al., 2015; Udall and Overpeck, 2017;
50 Ficklin et al., 2018; Xiao et al., 2018a; Glas et al., 2019; Martin et al., 2020; Milly and Dunne, 2020).
51
52 In other regions the influence of human water uses can be more important to explain hydrological drought
53 trends (Liu et al., 2016b; Mohammed and Scholz, 2016). There is medium confidence that human-induced
54 climate change has contributed to an increase of hydrological droughts in the Mediterranean (Giuntoli et al.,
55 2013; Vicente-Serrano et al., 2014; Gudmundsson et al., 2017), but also medium confidence that changes in
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1 land use and terrestrial water management contributed to these trends as well (Teuling et al., 2019; Vicente-
2 Serrano et al., 2019; Section 11.9). A global study with a single hydrological model estimated that human
3 water consumption has intensified the magnitude of hydrological droughts by 20%-40% over the last 50
4 years, and that the human water use contribution to hydrological droughts was more important than climatic
5 factors in the Mediterranean, and the central US, as well as in parts of Brazil (Wada et al., 2013). However,
6 Gudmundsson et al. (2021) concluded that the contribution of human water use is smaller than that of
7 anthropogenic climate change to explain spatial differences in the trends of low flows based on a multi-
8 model analysis. There is still limited evidence and thus low confidence in assessing these trends at the scale
9 of single regions, with few exceptions (Section 11.9).
10
11
12 11.6.4.4 Atmospheric-based drought indices
13
14 Different studies using atmospheric-based drought indices suggest an attributable anthropogenic signal,
15 characterized by the increased frequency and severity of droughts (Cook et al., 2018), associated to increased
16 AED (Section 11.6.4.2). The majority of studies are based on the PDSI-PM. Williams et al. (2015) and
17 Griffin and Anchukaitis (2014) concluded that increased AED has had an increased contribution to drought
18 severity over the last decades, and played a dominant role in the intensification of the 2012-2014 drought in
19 California. The same temporal pattern and physical mechanism was stressed by Li et al. (2017) in Central
20 Asia. Marvel et al. (2019) compared tree ring-based reconstructions of the PDSI-PM over the past
21 millennium with PDSI-PM estimates based on output from CMIP5 models, suggesting a contribution of
22 greenhouse gas forcing to the changes since the beginning of the 20th century, although characterized with
23 temporal differences that could be driven by temporal variations in the aerosol forcing, in agreement with the
24 dominant external forcings of aridification at global scale between 1950 and 2014 (Bonfils et al., 2020). In
25 the Mediterranean region there is medium confidence of drying attributable to antropogenic forcing as a
26 consequence of the strong AED increase (Gocic and Trajkovic, 2014; Liuzzo et al., 2014; Azorin-Molina et
27 al., 2015; Maček et al., 2018), which has enhanced the severity of drought events (Vicente-Serrano et al.,
28 2014; Stagge et al., 2017; González-Hidalgo et al., 2018). In particular, this effect was identified to be the
29 main driver of the intensification of the 2017 drought that affected southwestern Europe, and was attributed
30 to the human forcing (García-Herrera et al., 2019). Nangombe et al. (2020) and Zhang et al. (2020)
31 concluded from differences between precipitation and AED that anthropogenic forcing contributed to 2018
32 droughts that affected southern Africa and southeastern China, respectively, principally as consequence of
33 the high AED that characterised these two events.
34
35
36 11.6.4.5 Synthesis for different drought types
37
38 The regional evidence on attribution for single AR6 regions generally shows low confidence for a human
39 contribution to observed trends in meteorological droughts at regional scale, with few exceptions (Section
40 11.9). There is medium confidence that human influence has contributed to changes in agricultural and
41 ecological droughts and has led to an increase in the overall affected land area. At regional scales, there is
42 medium confidence in a contribution of human-induced climate change to increases in agricultural and
43 ecological droughts in the Mediterranean (MED) and Western North America (WNA) (Section 11.9). There
44 is medium confidence that human-induced climate change has contributed to an increase in hydrological
45 droughts in the Mediterranean region, but also medium confidence in contributions from other human
46 influences, including water management and land use (Section 11.9). Several meteorological and agricultural
47 and ecological drought events have been attributed to human-induced climate change, even in regions where
48 no long-term changes are detected (medium confidence). However, a lack of attribution to human-induced
49 climate change has also been shown for some events (medium confidence).
50
51 In summary, human influence has contributed to changes in water availability during the dry season over
52 land areas, including decreases over several regions due to increases in evapotranspiration (medium
53 confidence). The increases in evapotranspiration have been driven by increases in atmospheric evaporative
54 demand induced by increased temperature, decreased relative humidity and increased net radiation over
55 affected land areas (high confidence). There is low confidence that human influence has affected trends in
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1 meteorological droughts in most regions, but medium confidence that they have contributed to the severity of
2 some single events. There is medium confidence that human-induced climate change has contributed to
3 increasing trends in the probability or intensity of recent agricultural and ecological droughts, leading to an
4 increase of the affected land area. Human-induced climate change has contributed to global-scale change in
5 low flow, but human water management and land use changes are also important drivers (medium
6 confidence).
7
8
9 11.6.5 Projections
10
11 SREX (Chapter 3) asssessed with medium confidence projections of increased drought severity in some
12 regions, including southern Europe and the Mediterranean, central Europe, Central America and Mexico,
13 northeast Brazil, and southern Africa, and. low confidence elsewhere given large inter-model spread. AR5
14 (Chapters 11 and 12) also assessed large uncertainties in drought projections at the regional and global
15 scales. The assessment of drought mechanisms under future climate change scenarios depends on the model
16 used (Section 11.6.3). Moreover, uncertainties in drought projections are affected by the consideration of
17 plant physiological responses to increasing atmospheric CO2 (Greve et al., 2019; Mankin et al., 2019; Milly
18 and Dunne, 2016; Yang et al., 2020; Chapter 5, Cross-Chapter Box 5.1), the role of soil moisture-atmosphere
19 feedbacks for changes in water-balance and aridity (Berg et al., 2016; Zhou et al., 2021), and statistical
20 issues related to considered drought time scales (Vicente-Serrano et al., 2020a). Nonetheless, the extensive
21 literature available since AR5 allows a substantially more robust assessment of projected changes in
22 droughts, also subdivided in different drought types (meteorological drought, agricultural and ecological
23 drought, and hydrological drought). This includes assessments of projected changes in droughts, including
24 changes at 1.5°C, 2°C and 4°C of global warming, for all AR6 regions (Section 11.9). Projected changes
25 show increases in drought frequency and intensity in several regions as function of global warming (high
26 confidence). There are also substantial increases in drought hazard probability from 1.5°C to 2°C global
27 warming as well as for further additional increments of global warming (Figs. 11.18 and 11.19) (high
28 confidence). These findings are based both on CMIP5 and CMIP6 analyses (Section 11.9; Greve et al., 2018;
29 Wartenburger et al., 2017; Xu et al., 2019a), and strengthen the conclusions of the SR15 Ch3.
30
31
32 11.6.5.1 Precipitation deficits
33
34 Studies based on CMIP5, CMIP6 and CORDEX projections show a consistent signal in the sign and spatial
35 pattern of projections of precipitation deficits. Global studies based on these multi-model ensemble
36 projections (Orlowsky and Seneviratne, 2013; Martin, 2018; Spinoni et al., 2020; Ukkola et al., 2020;
37 Coppola et al., 2021b) show particularly strong signal-to-noise ratios for increasing meteorological droughts
38 in the following AR6 regions: MED, ESAF, WSAF, SAU, CAU, NCA, SCA, NSA and NES (Section 11.9).
39 There is also substantial evidence of changes in meteorological droughts at 1.5°C vs 2°C of global warming
40 from global studies (Wartenburger et al., 2017; Xu et al., 2019a). The patterns of projected changes in mean
41 precipitation are consistent with the changes in the drought duration, but they are not consistent with the
42 changes in drought intensity (Ukkola et al., 2020). In general, CMIP6 projections suggest a stronger increase
43 of the probability of precipitation deficits than CMIP5 projections (Cook et al., 2020; Ukkola et al., 2020).
44 Projections for the number of CDDs in CMIP6 (Figure 11.19) for different levels of global warming relative
45 to 1850-1900 show similar spatial patterns as projected precipitation deficits. The robustness of the patterns
46 in projected precipitation deficits identified in the global studies is also consistent with results from regional
47 studies (Giorgi et al., 2014; Marengo and Espinoza, 2016; Pinto et al., 2016; Huang et al., 2018a; Maúre et
48 al., 2018; Nangombe et al., 2018; Tabari and Willems, 2018; Abiodun et al., 2019; Dosio et al., 2019).
49
50 In Africa, a strong increase in the length of dry spells (CDD) is projected for 4°C of global warming over
51 most of the continent with the exception of central and eastern Africa (Giorgi et al., 2014; Han et al., 2019;
52 Sillmann et al., 2013; Section 11.9). In West Africa, a strong reduction of precipitation is projected
53 (Sillmann et al., 2013a; Diallo et al., 2016; Akinsanola and Zhou, 2018; Han et al., 2019; Todzo et al., 2020)
54 at 4°C of global warming, and CDD would increase with stronger global warming levels (Klutse et al.,
55 2018). The regions most strongly affected are Southern Africa (ESAF, WSAF; (Nangombe et al., 2018;
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1 Abiodun et al., 2019) and Northern Africa (part of MED region), with increases in meteorological droughts
2 already at 1.5°C of global warming, and further increases with increasing global warming (Section 11.9).
3 CDD is projected to increase more in the southern Mediterranean (northern Africa) than in the northern part
4 of the Mediterranean region (Lionello and Scarascia, 2020).
5
6 In Asia, most AR6 regions show low confidence in projected changes in meteorological droughts at 1.5°C
7 and 2°C of global warming, with a few regions displaying a decrease in meteorological droughts at 4°C of
8 global warming (RAR, ESB, RFE, ECA; medium confidence), although there is a projected increase in
9 meteorological droughts in Southeast Asia (SEA) at 4°C (medium confidence) (Section 11.9). In Southeast
10 Asia, an increasing frequency of precipitation deficits is projected as a consequence of an increasing
11 frequency of extreme El Niño (Cai et al., 2014a, 2015, 2018).
12
13 In central America, projections suggest an increase in mid-summer meteorological drought (Imbach et al.,
14 2018) and increased CDD (Nakaegawa et al., 2013; Chou et al., 2014a; Giorgi et al., 2014). In the Amazon,
15 there is also a projected increase in dryness (Marengo and Espinoza, 2016), which is the combination of a
16 projected increase in the frequency and geographic extent of meteorological drought in the eastern Amazon,
17 and an opposite trend in the West (Duffy et al., 2015). In southwestern South America, there is a projected
18 increase of the CDD (Chou et al., 2014a; Giorgi et al., 2014) and in Chile, drying is projected to prevail
19 (Boisier et al., 2018). In the South America monsoon region, an increase in CDD is projected (Chou et al.,
20 2014a; Giorgi et al., 2014), but a decrease is projected in southeastern and southern South America (Giorgi et
21 al., 2014). In Central America, mid summer meteorological drought is projected to intensify during 2071-
22 2095 for the RCP8.5 scenario (Corrales-Suastegui et al., 2019).
23
24 An increase in the frequency, duration and intensity of meteorological droughts is projected in southwest,
25 south and east Australia (Kirono et al., 2020; Shi et al., 2020). In Canada and most of the USA, and based on
26 the SPI, Swain and Hayhoe (2015) identified drier summer conditions in projections over most of the region,
27 and there is a consistent signal toward an increase in duration and intensity of droughts in southern North
28 America (Pascale et al., 2016; Escalante-Sandoval and Nuñez-Garcia, 2017). In California, more
29 precipitation variability is projected, characterised by increased frequency of consecutive drought and humid
30 periods (Swain et al., 2018).
31
32 Substantial increases in meteorological drought are projected in Europe, in particular in the Mediterranean
33 region already at 1.5°C of global warming (Section 11.9). In southern Europe, model projections display a
34 consistent drying among models (Russo et al., 2013; Hertig and Tramblay, 2017; Guerreiro et al., 2018a;
35 Raymond et al., 2019). In Western and Central Europe there is some spread in CMIP5 projections, with
36 some models projecting very strong drying and others close to no trend (Vogel et al., 2018), although CDD
37 is projected to increase in CMIP5 projections under the RCP 8.5 scenario (Hari et al., 2020). The overall
38 evidence suggests an increase in meteorological drought at 4°C in the WCE region (medium confidence;
39 Section 11.9).
40
41 Overall, based on both global and regional studies, several hot spot regions are identified displaying more
42 frequent and severe meteorological droughts with increasing with global warming, including several AR6
43 regions at 1.5°C (WSAF, ESAF, SAU, MED, NES) and 2°C of global warming (WSAF, ESAF, EAU, SAU,
44 MED, NCA, SCA, NSA, NES) (Section 11.9). At 4°C of global warming, there is also confidence in
45 increases in meteorological droughts in further regions (WAF, WCE, ENA, CAR, NWS, SAM, SWS, SSA;
46 Section 11.9), showing a geographical expansion of meteorological drought with increasing global warming.
47 Only few regions are projected to have less intense or frequent meteorological droughts (Section 11.9).
48
49
50 11.6.5.2 Atmospheric evaporative demand
51
52 Effects of AED on droughts in future projections is under debate. CMIP5 models project an AED increase
53 over the majority of the world with increasing global warming, mostly as a consequence of strong VPD
54 increases (Scheff and Frierson, 2015; Vicente-Serrano et al., 2020b). However, ET is projected to increase
55 less than AED in many regions, due to plant physiological responses related to i) CO2 effects on plant
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1 photosynthesis and ii) soil moisture control on ET.
2
3 Several studies suggest that increasing atmospheric CO2 could lead to reduced leaf stomatal conductance,
4 which would increase water-use efficiency and reduce plant water needs, thus limiting ET (Chapter 5, Cross-
5 Chapter Box 5.1; Greve et al., 2017; Lemordant et al., 2018; Milly and Dunne, 2016; Roderick et al., 2015;
6 Scheff et al., 2017; Swann, 2018; Swann et al., 2016). The implemention of a CO2-dependent land resistance
7 parameter has been suggested for the estimation of AED (Yang et al., 2019). Nevertheless, there are other
8 relevant mechanisms, as soil moisture deficits and VPD also play an importantrole in the control of the leaf
9 stomatal conductance (Xu et al., 2016b; Menezes-Silva et al., 2019; Grossiord et al., 2020) and a number of
10 ecophysiological and anatomical processes affect the response of plant physiology under higheratmospheric
11 CO2 concentrations (Mankin et al., 2019; Menezes-Silva et al., 2019; Chapter 5, Cross-Chapter Box 5.1).
12 The benefits of the atmospheric CO2 for plant stress and agricultural and ecological droughts would be
13 minimal precisely during dry periods given stomatal closure in response to limited soil moisture (Allen et al.,
14 2015; Xu et al., 2016b). In addition, CO2 effects on plant stomatal conductance could not entirely
15 compensate the increased demand associated to warming (Liu and Sun, 2017); in large tropical and
16 subtropical regions (e.g. southern Africa, the Amazon, the Mediterranean and southern North America),
17 AED is projected to increase even considering the possible CO2 effects on the land resistance (Vicente-
18 Serrano et al., 2020b). Moreover, these CO2 effects would not affect the direct evaporation from soils and
19 water bodies, which is very relevant in the reservoirs of warm areas (Friedrich et al., 2018). Because of these
20 uncertainties, there is low confidence whether increased CO2-induced water-use efficiency in vegetation will
21 substantially reduce global plant transpiration and will diminish the frequency and severity of soil moisture
22 and streamflow deficits associated with the radiative effect of higher CO2 concentrations (Chapter 5, CC Box
23 5.1).
24
25 Another mechanism reducing the ET response to increased AED in projections is the control of soil moisture
26 limitations on ET, which leads to reduced stomatal conductance under water stress (Berg and Sheffield,
27 2018; Stocker et al., 2018; Zhou et al., 2021). This response may be further amplified through VPD-induced
28 decreases in stomatal conductance (Anderegg et al., 2020). However, the decreased stomatal conductance in
29 response to both soil moisture limitation and enhanced CO2 would further enhance AED (Sherwood and Fu,
30 2014; Berg et al., 2016; Teuling, 2018; Miralles et al., 2019), whereby the overall effects on AED in ESMs
31 are found to be of similar magnitude for soil moisture limitation and CO2 physiological effects on stomatal
32 conductance (Berg et al., 2016). Increased AED is thus both a driver and a feedback with respect to changes
33 in ET, complicating the interpretation of its role on drought changes with increasing CO2 concentrations and
34 global warming.
35
36
37 11.6.5.3 Soil moisture deficits
38
39 Areas with projected soil moisture decreases do not fully coincide with areas with projected precipitation
40 decreases, although there is substantial consistency in the respective patterns (Dirmeyer et al., 2013; Berg
41 and Sheffield, 2018). There are, however, more regions affected by increased soil moisture deficits (Figure
42 11.19) than precipitation deficits (CC-Box 11.1, Figures 2a,b,c), as a consequence of enhanced AED and the
43 associated increased ET, as highlighted by some studies (Dai et al., 2018; Orlowsky and Seneviratne, 2013;
44 Chapter 8, Section 8.2.2.1). Moisture in the top soil layer is projected to decrease more than precipitation at
45 all warming levels (Lu et al., 2019), extending the regions affected by severe soil moisture deficits over most
46 of south and central Europe (Lehner et al., 2017; Ruosteenoja et al., 2018; Samaniego et al., 2018; Van Der
47 Linden et al., 2019), southern North America (Cook et al., 2019), South America (Orlowsky and
48 Seneviratne, 2013), southern Africa (Lu et al., 2019), East Africa (Rowell et al., 2015), southern Australia
49 (Kirono et al., 2020), India (Mishra et al., 2014b) and East Asia (Cheng et al., 2015) (Figure 11.19).
50 Projected changes in total soil moisture display less widespread drying than those for surface soil moisture
51 (Berg et al., 2017b), but still more than for precipitation (CC-Box 11.1, Figures 2a,b,c). The severity of
52 droughts based on surface soil moisture in future projections is stronger than projections based on
53 precipitation and runoff (Dai et al., 2018; Vicente-Serrano et al., 2020a). Nevertheless, in many parts of the
54 world in which soil moisture is projected to decrease, the signal to noise ratio among models is low and only
55 in the Mediterranean, Europe, the southwestern United States, and southern Africa the projections show a
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1 high signal to noise ratio in soil moisture projections (Lu et al., 2019; (Figure 11.19). Increases in soil
2 moisture deficits are found to be statistically signicant at regional scale in the Mediterranean region,
3 Southern Africa and Western South America for changes as small as 0.5°C in global warming, based on
4 differences between +1.5°C and +2°C of global warming (Wartenburger et al., 2017). Several other regions
5 are affected when considering changes in droughts for higher changes in global warming (Figure 11.19;
6 Section 11.9). Seasonal projections of drought frequency for boreal winter (DJF) and summer (JJA), from
7 CMIP6 multimodel ensemble for 1.5ºC, 2ºC and 4ºC global warming levels, show contrasting trends (Fig
8 11.19). In the boreal winter in the Northern Hemisphere, the areas affected by drying show high agreement
9 with those characterized by increase in meteorological drought projections (Chapter 8, Figure 8.14; Chapter
10 12, Figure 12.4). On the contrary, in the boreal summer the drought frequency increases worldwide in
11 comparison to meteorological drought projections, with large areas of the Northern Hemisphere displaying a
12 high signal to noise ratio (low spead between models). This stresses the dominant influence of ET (as a result
13 of increased AED) in intensifying agricultural and ecological droughts in the warm season in many locations,
14 including mid- to high latitudes.
15
16 Increased soil moisture limitation and associated changes in droughts are projected to lead to increased
17 vegetation stress affecting the global land carbon sink in ESM projections (Green et al., 2019), with
18 implications for projected global warming (Cross-Chapter Box 5). There is high confidence that the global
19 land sink will become less efficient due to soil moisture limitations and associated agricultural and
20 ecological drought conditions in some regions in higher emission scenarios specially under global warming
21 levels above 4°C ; however, there is low confidence on how these water cycle feedbacks will play out in
22 lower emission scenarios (at 2°C global warming or lower) (Cross-Chapter Box5.1).
23
24
25 [START FIGURE 11.18 HERE]
26
27 Figure 11.18:Projected changes in the intensity (a) and frequency (b) of drought under 1°C, 1.5°C, 2°C, 3°C, and 4°C
28 global warming levels relative to the 1850-1900 baseline. Summaries are computed for the AR6 regions
29 in which there is at least medium confidence in increase in agriculture/ ecological drought at the 2°C
30 warming level (“drying regions”), including W. North-America, C. North-America, N. Central-America,
31 S. Central-America, N. South-America, N. E. South-America, South-American-Monsoon, S.W.South-
32 America, S.South-America, West & Central-Europe, Mediterranean, W.Southern-Africa, E.Southern-
33 Africa, Madagascar, E.Australia, S.Australia (c). A drought event is defined as a 10-year drought event
34 whose annual mean soil moisture was below its 10th percentile from the 1850-1900 base period. For each
35 box plot, the horizontal line and the box represent the median and central 66% uncertainty range,
36 respectively, of the frequency or the intensity changes across the multi-model ensemble, and the whiskers
37 extend to the 90% uncertainty range. The line of zero in (a) indicates no change in intensity, while the
38 line of one in (b) indicates no change in frequency. The results are based on the multi-model ensemble
39 estimated from simulations of global climate models contributing to the sixth phase of the Coupled Model
40 Intercomparison Project (CMIP6) under different SSP forcing scenarios. Intensity changes in (a) are
41 expressed as standard deviations of the interannualvariability in the period 1850-1900 of the
42 corresponding modelFor details on the methods see Supplementary Material 11.SM.2. Further details on
43 data sources and processing are available in the chapter data table (Table 11.SM.9).
44
45 [END FIGURE 11.18 HERE]
46
47
48 11.6.5.4 Hydrological deficits
49
50 Some studies support wetting tendencies as a response to a warmer climate when considering globally-
51 averaged changes in runoff over land (Roderick et al., 2015; Greve et al., 2017; Yang et al., 2018e), and
52 streamflow projections respond to enhanced CO2 concentrations in CMIP5 models (Yang et al., 2019).
53 Nevertheless, when focusing regionally on low-runoff periods, model projections also show an increase of
54 hydrological droughts in large world regions (Wanders and Van Lanen, 2015; Dai et al., 2018; Vicente-
55 Serrano et al., 2020a). In general, the frequency of hydrological deficits is projected to increase over most of
56 the continents, although with regionally and seasonally differentiated effects (Section 11.9), with medium
57 confidence of increase in the following AR6 regions:WCE, MED, SAU, WCA, WNA, SCA, NSA, SAM,
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1 SWS, SSA, WSAF, ESAF and MDG (Section 11.9; Cook et al., 2019; Forzieri et al., 2014; Giuntoli et al.,
2 2015; Marx et al., 2018; Prudhomme et al., 2014; Roudier et al., 2016; Wanders and Van Lanen, 2015; Zhao
3 et al., 2020). There are, however, large uncertainties related to the hydrological/impact model used
4 (Prudhomme et al., 2014; Schewe et al., 2014; Gosling et al., 2017), limited signal-to-noise ratio (due to
5 model spread) in several regions (Giuntoli et al., 2015), and also uncertainties in the projection of future
6 human activities including water demand and land cover changes, which may represent more than 50% of
7 the projected changes in hydrological droughts in some regions (Wanders and Wada, 2015).
8
9 Regions dependent on mountainous snowpack as a temporary reservoir may be affected by severe
10 hydrological droughts in a warmer world. In the southern European Alps, both winter and summer low flows
11 are projected to be more severe, with a 25% decrease in the 2050s (Vidal et al., 2016). In the western United
12 States, a 22% reduction in winter snow water equivalent is projected at around 2°C of global warming with a
13 further decrease of a 70% reduction at 4°C global warming (Rhoades et al., 2018). This decline would cause
14 less predictable hydrological droughts in snowmelt-dominated areas of North America (Livneh and Badger,
15 2020). The exact magnitude of the influence of higher temperatures on snow-related droughts is, however,
16 difficult to estimate (Mote et al., 2016), since the streamflow changes could affect the timing of peak
17 streamflows but not necessarily their magnitude. In addition, projected changes in hydrological droughts
18 downstream of declining glaciers can be very complex to assess (Chapter 9, see also SROCC).
19
20
21 11.6.5.5 Atmospheric-based drought indices
22
23 Studies show a stronger drying in projections based on atmospheric-based drought indices compared to ESM
24 projections of changes in soil moisture (Berg and Sheffield, 2018) and runoff (Yang et al., 2019). It has been
25 suggested that this difference is due to physiological CO2 effects (Greve et al., 2019; Lemordant et al., 2018;
26 Milly and Dunne, 2016; Roderick et al., 2015; Scheff, 2018; Swann, 2018; Swann et al., 2016; Yang et al.,
27 2020; Section 11.6.5.2). Nonetheless, there is evidence that differences in projections between atmospheric-
28 based drought indices and water-balance metrics from ESMs are not alone due to CO2-plant effects (Berg et
29 al., 2016; Scheff et al., 2021), and can be also related to the fact that AED is an upper bound for ET in dry
30 regions and conditions (Section 11.6.1.2) and that soil moisture stress limits increases in ET in projections
31 (Berg et al., 2016; Zhou et al., 2021; Section 11.6.5.2). Atmospheric-based indices show in general more
32 drying than total column soil moisture (Berg and Sheffield, 2018; Cook et al., 2020; Scheff et al., 2021), but
33 are more consistent with projected increases in surface soil moisture deficits (Dirmeyer et al., 2013; Dai et
34 al., 2018; Lu et al., 2019; Cook et al., 2020; Vicente-Serrano et al., 2020a).
35
36 Atmospheric-based drought indices are not metrics of soil moisture or runoff (11.6.1.5) so their projections
37 may not necessarily reflect the same trend of online simulated soil moisture and runoff. Independently of
38 effects on the land water balance, atmospheric-based drought indices will reflect the potential vegetation
39 stress resulting from deficits between available water and enhanced AED, even in conditions with no or only
40 low ET. Under dry conditions, the enhanced AED associated to the human forcing would increase plant
41 water stress (Brodribb et al., 2020), with effects on widespread forest dieback and mortality (Anderegg et al.,
42 2013; Williams et al., 2013; Allen et al., 2015; McDowell and Allen, 2015; McDowell et al., 2016, 2020),
43 and stronger risk of megafires (Flannigan et al., 2016; Podschwit et al., 2018; Clarke and Evans, 2019;
44 Varela et al., 2019). For these reasons, there is high confidence that the future projections of enhanced
45 drought severity showed by the PDSI-PM and the SPEI-PM are representative of more frequent and severe
46 plant stress episodes and more severe agricultural and ecological drought impacts in some regions.
47
48 Global tendencies towards more severe and frequent agricultural and ecological drought conditions are
49 identified in future projections when focusing on atmospheric-based drought indices such as the PDSI-PM or
50 the SPEI-PM. They expand the spatial extent of drought conditions compared to meteorological drought to
51 most of North America, Europe, Africa, Central and East Asia and southern Australia (Cook et al., 2014a;
52 Chen and Sun, 2017b, 2017a; Zhao and Dai, 2017; Gao et al., 2017b; Lehner et al., 2017; Dai et al., 2018;
53 Naumann et al., 2018; Potopová et al., 2018; Vicente-Serrano et al., 2020a; Gu et al., 2020; Dai, 2021).
54 Projections in PDSI-PM and SPEI-PM are used in complement to changes in total soil moisture for the
55 assessed projected changes in agricultural and ecological drought (Section 11.9).
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1
2
3 11.6.5.6 Synthesis for different drought types
4
5 The tables in Section 11.9 provide assessed projected changes in metorological drought, agricultural and
6 ecological drought, and hydrological droughts. The assessment shows that several regions will be affected by
7 more severe agricultural and ecological droughts even if global warming is stabilized at well below 2°C, and
8 1.5°C, within the bounds of the Paris Agreement (high confidence). The most affected regions include WCE,
9 MED, EAU, SAU, SCA, NSA, SAM, SWS, SSA, NCA, CAN, WSAF, ESAF and MDG (medium
10 confidence). At 4°C of global warming, even more regions would be affected by agricultural and ecological
11 droughts (WCE, MED, CAU, EAU, SAU, WCA, EAS, SCA, CAR, NSA, NES, SAM, SWS, SSA, NCA,
12 CAN, ENA, WNA, WSAF, ESAF and MDG). NEAF, SAS are also projected to experience less agricultural
13 and ecological drought with global warming (medium confidence). Projected changes in meteorological
14 droughts are overall less extended but also affect several AR6 regions, at 1.5°C and 2°C (MED, EAU, SAU,
15 SCA, NSA, NCA, WSAF, ESAF, MDG) and 4ºC of global warming (WCE, MED, EAU, SAU, SEA, SCA,
16 CAR, NWS, NSA, NES, SAM, SWS, SSA, NCA, ENA, WAF, WSAF, ESAF, MDG). Several regions are
17 also projected to be affected by more hydrological droughts at 1.5°C and 2°C (WCE, MED, WNA, WSAF,
18 ESAF) and 4ºC of global warming (NEU, WCE, EEU, MED, SAU, WCA, SCA, NSA, SAM, SWS, SSA,
19 WNA, WSAF, ESAF, MDG). To illustrate the changes in both intensity and frequency of drought in the
20 regions where strongest changes are projected, Figure 11.18 displays changes in the intensity and frequency
21 of soil moisture drought under different global warming levels (1.5°C, 2°C, 4°C) relative to the 1851-1900
22 baseline based on CMIP6 simulations under different SSP forcing scenarios. The 90% uncertainty ranges for
23 the projected changes in both intensity and frequency are above zero, indicating significant increase in both
24 intensity and frequency of drought in these regions as whole.
25
26 In summary, the land area affected by increasing drought frequency and severity expands with increasing
27 global warming (high confidence). New evidence strengthens the SR15 conclusion that even relatively small
28 incremental increases in global warming (+0.5°C) cause a worsening of droughts in some regions (high
29 confidence). Several regions will be affected by more frequent and severe agricultural and ecological
30 droughts even if global warming is stabilized at 1.5-2°C (high confidence). The most affected regions
31 include WCE, MED, EAU, SAU, SCA, NSA, SAM, SWS, SSA, NCA, CAN, WSAF, ESAF and MDG
32 (medium confidence). At 4°C of global warming, even more regions would be affected by agricultural and
33 ecological droughts (WCE, MED, CAU, EAU, SAU, WCA, EAS, SCA, CAR, NSA, NES, SAM, SWS,
34 SSA, NCA, CAN, ENA, WNA, WSAF, ESAF and MDG). Some regions are also projected to experience
35 less agricultural and ecological drought with global warming (medium confidence; (NEAF, SAS)). There is
36 high confidence that the projected increases in agricultural and ecological droughts are strongly affected by
37 AED increases in a warming climate, although ET increases are projected to be smaller than those in AED
38 due to soil moisture limitations and CO2 effects on leaf stomatal conductance. Enhanced atmospheric CO2
39 concentrations lead to enhanced water-use efficiency in plants (medium confidence), but there is low
40 confidence that it can ameliorate agricultural and ecological droughts, or hydrological droughts, at higher
41 global warming levels characterized by limited soil moisture and enhanced AED.
42
43 Projected changes in meteorological droughts are overall less extended than for agricultural and ecological
44 droughts, but also affect several AR6 regions, even at 1.5°C and 2°C of global warming. Several regions are
45 also projected to be more strongly affected by hydrological droughts with increasing global warming (NEU,
46 WCE, EEU, MED, SAU, WCA, SCA, NSA, SAM, SWS, SSA, WNA, WSAF, ESAF, MDG). Increased
47 soil moisture limitation and associated changes in droughts are projected to lead to increased vegetation
48 stress in many regions, with implications for the global land carbon sink (CC-Box 5). There is high
49 confidence that the global land sink will become less efficient due to soil moisture limitations and associated
50 drought conditions in some regions in higher emission scenarios specially under global warming levels
51 above 4°C ; however, there is low confidence on how these water cycle feedbacks will play out in lower
52 emission scenarios (at 2°C global warming or lower) (Cross-Chapter Box5.1).
53
54
55 [START FIGURE 11.19 HERE]
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1
2 Figure 11.19:Projected changes in (a-c) the number of consecutive dry days (CDD), (d-f) annual mean soil moisture
3 over the total column, and (g-l) the frequency and intensity of one-in-ten year soil moisture drought for
4 the June-to-August and December-to-February seasons at 1.5°C, 2°C, and 4°C of global warming
5 compared to the 1851-1900 baseline. Results are based on simulations from the CMIP6 multi-model
6 ensemble under the SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios. The numbers in
7 the top right indicate the number of simulations included. Uncertainty is represented using the simple
8 approach: no overlay indicates regions with high model agreement, where ≥80% of models agree on sign
9 of change; diagonal lines indicate regions with low model agreement, where <80% of models agree on
10 sign of change. For more information on the simple approach, please refer to the Cross-Chapter Box
11 Atlas 1. For details on the methods see Supplementary Material 11.SM.2. Further details on data sources
12 and processing are available in the chapter data table (Table 11.SM.9).
13
14
15 [END FIGURE 11.19 HERE]
16
17
18 11.7 Extreme storms
19
20 Extreme storms, such as tropical cyclones (TCs), extratropical cyclones (ETCs), and severe convective
21 storms often have substantial societal impacts. Quantifying the effect of climate change on extreme storms is
22 challenging, partly because extreme storms are rare, short-lived, and local, and individual events are largely
23 influenced by stochastic variability. The high degree of random variability makes detection and attribution of
24 extreme storm trends more uncertain than detection and attribution of trends in other aspects of the
25 environment in which the storms evolve (e.g., larger-scale temperature trends). Projecting changes in
26 extreme storms is also challenging because of constraints in the models' ability to accurately represent the
27 small-scale physical processes that can drive these changes. Despite the challenges, progress has been made
28 since AR5.
29
30 SREX (Chapter 3) concluded that there is low confidence in observed long-term (40 years or more) trends in
31 TC intensity, frequency, and duration, and any observed trends in phenomena such as tornadoes and hail; it
32 is likely that extratropical storm tracks have shifted poleward in both the Northern and Southern
33 Hemispheres and that heavy rainfalls and mean maximum wind speeds associated with TCs will increase
34 with continued greenhouse gas (GHG) warming; it is likely that the global frequency of TCs will either
35 decrease or remain essentially unchanged, while it is more likely than not that the frequency of the most
36 intense storms will increase substantially in some ocean basins; there is low confidence in projections of
37 small-scale phenomena such as tornadoes and hail storms; and there is medium confidence that there will be
38 a reduced frequency and a poleward shift of mid-latitude cyclones due to future anthropogenic climate
39 change.
40
41 Since SREX, several IPCC assessments also assessed storms. AR5 (Chapter 2, Hartmann et al., 2013)
42 assessment with low confidence observed long-term trends in TC metrics, but revised the statement from
43 SREX to state that it is virtually certain that there are increasing trends in North Atlantic TC activity since
44 the 1970s, with medium confidence that anthropogenic aerosol forcing has contributed to these trends. AR5
45 concluded that it is likely that TC precipitation and mean intensity will increase and more likely than not that
46 the frequency of the strongest storms increases with continued GHG warming. Confidence in projected
47 trends in overall TC frequency remained low. Confidence in observed and projected trends in hail storm and
48 tornado events also remained low. SROCC (Chapter 6, Collins et al., 2019) assessed past and projected TCs
49 and ETCs supporting the conclusions of AR5 with some additional detail. Literature subsequent to AR5 adds
50 support to the likelihood of increasing trends in TC intensity, precipitation, and frequency of the most intense
51 storms, while some newer studies have added uncertainty to projected trends in overall frequency. A
52 growing body literature since AR5 on the poleward migration of TCs led to a new assessment in SROCC of
53 low confidence that the migration in the western North Pacific represents a detectable climate change
54 contribution from anthropogenic forcing. SR15 (Chapter 3, Hoegh-Guldberg et al., 2018) essentially
55 confirmed the AR5 assessment of TCs and ETCs, adding that heavy precipitation associated with TCs is
56 projected to be higher at 2°C compared to 1.5°C global warming (medium confidence).
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1
2 SREX, AR5, SROCC, and SR15, do not provide assessments of the atmospheric rivers and SROCC and
3 SR15 do not assess severe convective storms and extreme winds. This section assesses the state of
4 knowledge on the four phenomena of TCs, ETCs, severe convective storms, and extreme winds.
5 Atmospheric rivers are addressed in Chapter 8. In this respect, this assessment closely mirrors the SROCC
6 assessment of TCs and ETCs, while updating SREX and AR5 assessments of severe convective storms and
7 extreme winds.
8
9
10 11.7.1 Tropical cyclones
11
12 11.7.1.1 Mechanisms and drivers
13
14 The genesis, development, and tracks of TCs depend on conditions of the larger-scale circulations of the
15 atmosphere and ocean (Christensen et al., 2013). Large-scale atmospheric circulations (Annex VI), such as
16 the Hadley and Walker circulations and the monsoon circulations, and internal variability acting on various
17 time-scales, from intra-seasonal (e.g., the Madden-Julian and Boreal Summer Intraseasonal oscillations
18 (MJO, BSISO), and equatorial waves) and inter-annual (e.g., the El Niño-Southern Oscillation (ENSO) and
19 Pacific and Atlantic Meridional Modes (PMM, AMM)), to inter-decadal (e.g., Atlantic Multidecadal
20 Variability and Pacific Decadal Variability (PDV)) can all significantly affect TCs. This broad range of
21 natural variability makes detection of anthropogenic effects difficult, and uncertainties in the projected
22 changes of these modes of variability increase uncertainty in the projected changes in TC activity. Aerosol
23 forcing also affects SST patterns and cloud microphysics, and it is likely that observed changes in TC activity
24 are partly caused by changes in aerosol forcing (Evan et al., 2011; Ting et al., 2015; Sobel et al., 2016, 2019;
25 Takahashi et al., 2017; Zhao et al., 2018; Reed et al., 2019). Among possible changes from these drivers,
26 there is medium confidence that the Hadley cell has widened and will continue to widen in the future
27 (Chapter 2.3, 3.3, 4.5). This likely causes latitudinal shifts of TC tracks (Sharmila and Walsh, 2018).
28 Regional TC activity changes are also strongly affected by projected changes in SST warming patterns
29 (Yoshida et al., 2017), which are highly uncertain (Chapter 4, 9).
30
31
32 11.7.1.2 Observed trends
33
34 Identifying past trends in TC metrics remains a challenge due to the heterogeneous character of the historical
35 instrumental data, which are known as “best-track” data (Schreck et al., 2014). There is low confidence in
36 most reported long-term (multidecadal to centennial) trends in TC frequency- or intensity-based metrics due
37 to changes in the technology used to collect the best-track data. This should not be interpreted as implying
38 that no physical (real) trends exist, but rather as indicating that either the quality or the temporal length of the
39 data is not adequate to provide robust trend detection statements, particularly in the presence of multidecadal
40 variability.
41
42 There are previous and ongoing efforts to homogenize the best-track data (Elsner et al., 2008; Kossin et al.,
43 2013, 2020; Choy et al., 2015; Landsea, 2015; Emanuel et al., 2018) and there is substantial literature that
44 finds positive trends in intensity-related metrics in the best-track during the “satellite period”, which is
45 generally limited to the past ~40 years (Kang and Elsner, 2012; Kishtawal et al., 2012; Kossin et al., 2013,
46 2020; Mei and Xie, 2016; Zhao et al., 2018; Tauvale and Tsuboki, 2019). When best-track trends are tested
47 using homogenized data, the intensity trends generally remain positive, but are smaller in amplitude (Kossin
48 et al., 2013; Holland and Bruyère, 2014). Kossin et al. (2020) extended the homogenized TC intensity record
49 to the period 1979–2017 and identified significant global increases in major TC exceedance probability of
50 about 6% per decade. In addition to trends in TC intensity, there is evidence that TC intensification rates and
51 the frequency of rapid intensification events have increased within the satellite era (Kishtawal et al. 2012;
52 Balaguru et al., 2018; Bhatia et al., 2018). The increase in intensification rates is found in the best-track as
53 well as the homogenized intensity data.
54
55 A subset of the best-track data corresponding to hurricanes that have directly impacted the United States
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1 since 1900 is considered to be reliable, and shows no trend in the frequency of U.S. landfall events (Knutson
2 et al., 2019). However, in this period since 1900, an increasing trend in normalized U.S. hurricane damage,
3 which accounts for temporal changes in exposed wealth (Grinsted et al., 2019), and a decreasing trend in TC
4 translation speed over the U.S. (Kossin, 2019) have been identified. A similarly reliable subset of the data
5 representing TC landfall frequency over Australia shows a decreasing trend in eastern Australia since the
6 1800s (Callaghan and Power, 2011), as well as in other parts of Australia since 1982 (Chand et al., 2019;
7 Knutson et al., 2019), and a paleoclimate proxy reconstruction shows that recent levels of TC interactions
8 along parts of the Australian coastline are the lowest in the past 550–1,500 years (Haig et al., 2014). Existing
9 TC datasets show substantial interdecadal variations in basin-wide TC frequency and intensity in the western
10 North Pacific, but a statistically significant northwestward shift in the western North Pacific TC tracks since
11 the 1980s (Lee et al., 2020b). In the case of the North Indian Ocean, analyses of trends are highly dependent
12 on the details of each analysis (e.g., pre- and/or post-monsoon season period, or Bay of Bengal and/or
13 Arabian Sea region). The most consistent trends are an increase in the occurrence of the most intense TCs
14 and a decrease in the overall TC frequency, in particular in the Bay of Bengal (Sahoo and Bhaskaran, 2016;
15 Balaji et al., 2018; Singh et al., 2019; Baburaj et al., 2020). In the South Indian Ocean (SIO), an increase in
16 the occurrence of the most intense TCs has been noted, however there are well-known data quality issues
17 there (Kuleshov et al., 2010; Fitchett, 2018). When the SIO data are homogenized, a significant increase is
18 found in the fractional proportion of global category 3-5 TC estimates to all category 1-5 estimates (Kossin
19 et al., 2020).
20
21 As with all confined regional analyses of TC frequency, it is generally unclear whether any identified
22 changes are due to a basin-wide change in TC frequency, or to systematic track shifts (or both). From an
23 impacts perspective, however, these changes over land are highly relevant and emphasize that large-scale
24 modifications in TC behaviour can have a broad spectrum of impacts on a regional scale.
25
26 Subsequent to AR5, two metrics that are argued to be comparatively less sensitive to data issues than
27 frequency- and intensity-based metrics have been analysed. Trends in these metrics have been identified over
28 the past ~70 years or more (Knutson et al., 2019). The first metric, the mean latitude where TCs reach their
29 peak intensity, exhibits a global and regional poleward migration during the satellite period (Kossin et al.,
30 2014). The poleward migration can influence TC hazard exposure and risk (Kossin et al., 2016a) and is
31 consistent with the independently-observed expansion of the tropics (Lucas et al., 2014). The migration has
32 been linked to changes in the Hadley circulation (Altman et al., 2018; Sharmila and Walsh, 2018; Studholme
33 and Gulev, 2018). The migration is also apparent in the mean locations where TCs exhibit eyes (Knapp et al.,
34 2018), which is when TCs are most intense. Part of the Northern Hemisphere poleward migration is due to
35 inter-basin changes in TC frequency (Kossin et al., 2014, 2016b, Moon et al., 2015, 2016) and the trends, as
36 expected, can be sensitive to the time period chosen (Tennille and Ellis, 2017; Kossin, 2018; Song and
37 Klotzbach, 2018) and to subsetting of the data by intensity (Zhan and Wang, 2017). The poleward migration
38 is particularly pronounced and well-documented in the western North Pacific basin (Kossin et al., 2016a;
39 Oey and Chou, 2016; Liang et al., 2017; Nakamura et al., 2017; Altman et al., 2018; Daloz and Camargo,
40 2018; Sun et al., 2019b; Lee et al., 2020b; Yamaguchi and Maeda, 2020a; Kubota et al., 2021).
41
42
43 [START FIGURE 11.20 HERE]
44
45 Figure 11.20:Summary schematic of past and projected changes in tropical cyclone (TC), extratropical cyclone (ETC),
46 atmospheric river (AR), and severe convective storm (SCS) behaviour. Global changes (blue shading)
47 from top to bottom: 1) Increased mean and maximum rain-rates in TCs, ETCs, and ARs [past (low
48 confidence due to lack of reliable data) & projected (high confidence)]. 2) Increased proportion of
49 stronger TCs [past (medium confidence) & projected (high confidence)]. 3) Decrease or no change in
50 global frequency of TC genesis [past (low confidence due to lack of reliable data) & projected (medium
51 confidence)]. 4) Increased and decreased ETC wind-speed, depending on the region, as storm-tracks
52 change [past (low confidence due to lack of reliable data) & projected (medium confidence)]. Regional
53 changes, from left to right: 1) Poleward TC migration in the western North Pacific and subsequent
54 changes in TC exposure [past (medium confidence) & projected (medium confidence)]. 2) Slowdown of
55 TC forward translation speed over the contiguous US and subsequent increase in TC rainfall [past
56 (medium confidence) & projected (low confidence due to lack of directed studies)]. 3) Increase in mean
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1 and maximum SCS rain-rate and increase in springtime SCS frequency and season length over the
2 contiguous US [past (low confidence due to lack of reliable data) & projected (medium confidence)].
3
4 [END FIGURE 11.20 HERE]
5
6
7 A second metric that is argued to be comparatively less sensitive to data issues than frequency- and intensity-
8 based metrics is TC translation speed (Kossin, 2018), which exhibits a global slowdown in the best-track
9 data over the period 1949-2016. TC translation speed is a measure of the speed at which TCs move across
10 the Earth’s surface and is very closely related to local rainfall amounts (i.e., a slower translation speed causes
11 greater local rainfall). TC translation speed also affects structural wind damage and coastal storm surge by
12 changing the hazard event duration. The slowdown is observed in the best-track data from all basins except
13 the Northern Indian Ocean and is also found in a number of regions where TCs interact directly with land.
14 The slowing trends identified in the best-track data by Kossin (2018) have been argued to be largely due to
15 data heterogeneity. Moon et al. (2019) and Lanzante (2019) provide evidence that meridional TC track shifts
16 project onto the slowing trends and argue that these shifts are due to the introduction of satellite data. Kossin
17 (2019) provides evidence that the slowing trend is real by focusing on Atlantic TC track data over the
18 contiguous United States in the 118-year period 1900–2017, which are generally considered reliable. In this
19 period, mean TC translation speed has decreased by 17%. The slowing TC translation speed is expected to
20 increase local rainfall amounts, which would increase coastal and inland flooding. In combination with
21 slowing translation speed, abrupt TC track direction changes – that can be associated with track “meanders”
22 or “stalls” – have become increasingly common along the North American coast since the mid-20th century,
23 leading to more rainfall in the region (Hall and Kossin, 2019).
24
25 In summary, there is mounting evidence that a variety of TC characteristics have changed over various time
26 periods. It is likely that the proportion of major TC intensities and the frequency of rapid intensification
27 events have both increased globally over the past 40 years. It is very likely that the average location where
28 TCs reach their peak wind-intensity has migrated poleward in the western North Pacific Ocean since the
29 1940s. It is likely that TC translation speed has slowed over the U.S. since 1900.
30
31
32 11.7.1.3 Model evaluation
33
34 Accurate projections of future TC activity have two principal requirements: accurate representation of
35 changes in the relevant environmental factors (e.g., SSTs) that can affect TC activity, and accurate
36 representation of actual TC activity in given environmental conditions. In particular, models’ capacity to
37 reproduce historical trends or interannual variabilities of TC activity is relevant to the confidence in future
38 projections. One test of the models is to evaluate their ability to reproduce the dependency of the TC
39 statistics in the different basins in the real world, in addition to their capability of reproducing atmospheric
40 and ocean environmental conditions. For the evaluation of projections of TC-relevant environmental
41 variables, AR5 confidence statements were based on global surface temperature and moisture, but not on the
42 detailed regional structure of SST and atmospheric circulation changes such as steering flows and vertical
43 shear, which affect characteristics of TCs (genesis, intensity, tracks, etc.). Various aspects of TC metrics are
44 used to evaluate how capable models are of simulating present-day TC climatologies and variability (e.g. TC
45 frequency, wind-intensity, precipitation, size, tracks, and their seasonal and interannual changes) (Camargo
46 and Wing, 2016; Knutson et al., 2019, 2020; Walsh et al., 2015). Other examples of TC
47 climatology/variability metrics are spatial distributions of TC occurrence and genesis (Walsh et al., 2015),
48 seasonal cycles and interannual variability of basin-wide activity (Zhao et al., 2009; Shaevitz et al., 2014;
49 Kodama et al., 2015; Murakami et al., 2015; Yamada et al., 2017) or landfalling activity (Lok and Chan,
50 2018), as well as newly developed process-diagnostics designed specifically for TCs in climate models (Kim
51 et al., 2018a; Wing et al., 2019; Moon et al., 2020).
52
53 Confidence in the projection of intense TCs, such as those of Category 4-5, generally becomes higher as the
54 resolution of the models becomes higher. CMIP5/6 class climate models (~100-200 km grid spacing) cannot
55 simulate TCs of Category 4-5 intensity. They do simulate storms of relatively high vorticity that are at best
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1 described as “TC-like”, but metrics like storm counts are highly dependent on tracking algorithms (Wehner
2 et al., 2015; Zarzycki and Ullrich, 2017; Roberts et al., 2020a). High-resolution global climate models (~10-
3 60 km grid spacing) as used in HighResMIP (Haarsma et al., 2016; Roberts et al., 2020a) begin to capture
4 some structures of TCs more realistically, as well as produce intense TCs of Category 4-5 despite the effects
5 of parameterized deep cumulus convection processes (Murakami et al., 2015; Wehner et al., 2015; Yamada
6 et al., 2017; Roberts et al., 2018; Moon et al., 2020). Convection-permitting models (~1-10 km grid-
7 spacing), such as used in some dynamical downscaling studies, provide further realism with capturing eye
8 wall structures (Tsuboki et al., 2015). Model characteristics besides resolution, especially details of
9 convective parameterization, can influence a model’s ability to simulate intense TCs (Reed and Jablonowski,
10 2011; Zhao et al., 2012; He and Posselt, 2015; Kim et al., 2018a; Zhang and Wang, 2018; Camargo et al.,
11 2020). However, models’ dynamical cores and other physics also affect simulated TC properties (Reed et al.,
12 2015; Vidale et al., 2021). Both wide-area regional and global convection-permitting models without the
13 need for parameterized convection are becoming more useful for TC regional model projection studies
14 (Tsuboki et al., 2015; Kanada et al., 2017a; Gutmann et al., 2018) and global model projection studies (Satoh
15 et al., 2015, 2017; Yamada et al., 2017), as they capture more realistic eye-wall structures of TCs (Kinter et
16 al., 2013) and are becoming more useful for investigating changes in TC structures (Kanada et al., 2013;
17 Yamada et al., 2017). Large ensemble simulations of global climate models with 60 km grid spacing provide
18 TC statistics that allow more reliable detection of changes in the projections, which are not well captured in
19 any single experiment (Yoshida et al., 2017; Yamaguchi et al., 2020). Variable resolution global models
20 offer an alternative to regional models for individual TC or basin-wide simulations (Yanase et al., 2012;
21 Zarzycki et al., 2014; Harris et al., 2016; Reed et al., 2020; Stansfield et al., 2020). Computationally less
22 intense than equivalent uniform resolution global models, they also do not require lateral boundary
23 conditions, thus reducing this source of error (Hashimoto et al., 2016). Confidence in the projection of TC
24 statistics and properties is increased by the higher-resolution models with more realistic simulations.
25
26 Operational forecasting models also reproduce TCs and their use for climate projection studies shows
27 promise. However, there is limited application for future projections as they are specifically developed for
28 operational purposes and TC climatology is not necessarily well evaluated. Intercomparison of operational
29 models indicates that enhancement of horizontal resolution can provide more credible projections of TCs
30 (Nakano et al., 2017). Likewise, high-resolution climate models show promise as TC forecast tools (Zarzycki
31 and Jablonowski, 2015; Reed et al., 2020), further narrowing the continuum of weather and climate models
32 and increasing confidence in projections of future TC behaviour. However, higher horizontal resolution does
33 not necessarily lead to an improved TC climatology (Camargo et al., 2020).
34
35 Atmosphere-ocean interaction is an important process in TC evolution. Atmosphere-ocean coupled models
36 are generally better than atmosphere-only models at capturing realistic processes related to TCs (Murakami
37 et al., 2015; Ogata et al., 2015, 2016; Zarzycki, 2016; Kanada et al., 2017b; Scoccimarro et al., 2017),
38 although the basin-scale SST biases commonly found in atmosphere-ocean models can introduce substantial
39 errors in the simulated TC number (Hsu et al., 2019). Higher-resolution ocean models improve the
40 simulation of TCs by reducing the SST climatology bias (Li and Sriver, 2018; Roberts et al., 2020a). Coarse
41 resolution atmospheric models may degrade coupled model performance as well. For example, in a case
42 study of Hurricane Harvey, Trenberth et al. (2018) suggested that the lack of realistic hurricane activity
43 within coupled climate models hampers the models’ ability to simulate SST and ocean heat content and their
44 changes.
45
46 Even with higher-resolution atmosphere-ocean coupled models, TC projection studies still rely on
47 assumptions in experimental design that introduce uncertainties. Computational constraints often limit the
48 number of simulations, resulting in relatively small ensemble sizes and incomplete analyses of possible
49 future SST magnitude and pattern changes (Zhao and Held, 2011; Knutson et al., 2013a). Uncertainties in
50 aerosol forcing also are reflected in TC projection uncertainty (Wang et al., 2014).
51
52 Regional climate models (RCM) with grid spacing around 15-50 km can be used to study the projection of
53 TCs. RCMs are run with lateral and surface boundary conditions, which are specified by the atmospheric
54 state and SSTs simulated by GCMs. Various combinations of the lateral and surface boundary conditions can
55 be chosen for RCM studies, and uncertainties in the projection can be further examined in general. They are
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1 used for studying changes in TC characteristics in a specific area, such as Vietnam (Redmond et al., 2015)
2 and the Philippines (Gallo et al., 2019).
3
4 Less computationally-expensive downscaling approaches that allow larger ensembles and long-term studies
5 are also used in the projection of TCs (Emanuel et al., 2006; Lee et al., 2018a). A statistical–dynamical TC
6 downscaling method requires assumptions of the rate of seeding of random initial disturbances, which are
7 generally assumed to not change with climate change (Emanuel et al., 2008; Emanuel, 2013). The results
8 with the downscaling approach might depend on the assumptions which are required for the simplification of
9 the methods.
10
11 In summary, various types of models are useful to study climate changes of TCs, and there is no unique
12 solution for choosing a model type. However, higher-resolution models generally capture TC properties
13 more realistically (high confidence). In particular, models with horizontal resolutions of 10-60 km are
14 capable of reproducing strong TCs with Category 4-5 and those of 1-10km are capable of the eyewall
15 structure of TCs. Uncertainties in TC simulations come from details of the model configuration of both
16 dynamical and physical processes. Models with realistic atmosphere-ocean interactions are generally better
17 than atmosphere-only models at reproducing realistic TC evolutions (high confidence).
18
19
20 11.7.1.4 Detection and attribution, event attribution
21
22 There is general agreement in the literature that anthropogenic greenhouse gases and aerosols have
23 measurably affected observed oceanic and atmospheric variability in TC-prone regions (see Chapter 3). This
24 underpinned the SROCC assessment of medium confidence that humans have contributed to the observed
25 increase in Atlantic hurricane activity since the 1970s (Chapter 5, Bindoff et al., 2013). Literature subsequent
26 to the AR5 lends further support to this statement (Knutson et al., 2019). However, there is still no consensus
27 on the relative magnitude of human and natural influences on past changes in Atlantic hurricane activity, and
28 particularly on which factor has dominated the observed increase (Ting et al., 2015) and it remains uncertain
29 whether past changes in Atlantic TC activity are outside the range of natural variability. A recent result using
30 high-resolution dynamical model experiments suggested that the observed spatial contrast in TC trends
31 cannot be explained only by multi-decadal natural variability, and that external forcing plays an important
32 role (Murakami et al., 2020). Observational evidence for significant global increases in the proportion of
33 major TC intensities (Kossin et al., 2020) is consistent with both theory and numerical modeling simulations,
34 which generally indicate an increase in mean TC peak intensity and the proportion of very intense TCs in a
35 warming world (Knutson et al., 2015, 2020, Walsh et al., 2015, 2016). In addition, high-resolution coupled
36 model simulations provide support that natural variability alone is unlikely to explain the magnitude of the
37 observed increase in TC intensification rates and upward TC intensity trend in the Atlantic basin since the
38 early 1980s (Bhatia et al., 2019; Murakami et al. 2020).
39
40 The cause of the observed slowdown in TC translation speed is not yet clear. Yamaguchi et al. (2020) used
41 large ensemble simulations to argue that part of the slowdown is due to actual latitudinal shifts of TC tracks,
42 rather than data artefacts, in addition to atmospheric circulation changes, while Zhang et al. (2020a) used
43 large ensemble simulations to show that anthropogenic forcing can lead to a robust slowdown, particularly
44 outside of the tropics at higher latitudes. Yamaguchi and Maeda (2020b) found a significant slowdown in the
45 western North Pacific over the past 40 years and attributed the slowdown to a combination of natural
46 variability and global warming. The slowing trend since 1900 over the U.S. is robust and significant after
47 removing multidecadal variability from the time series (Kossin, 2019). Among the hypotheses discussed is
48 the physical linkage between warming and slowing circulation (Held and Soden 2006, see also Section
49 8.2.2.2), with expectations of Arctic amplification and weakening circulation patterns through weakening
50 meridional temperature gradients (Coumou et al., 2018; see also Cross-Chapter Box 10.1), or through
51 changes in planetary wave dynamics (Mann et al., 2017). The tropics expansion and the poleward shift of the
52 mid-latitude westerlies associated with warming is also suggested for the reason of the slowdown (Zhang et
53 al., 2020a). However, the connection of these mechanisms to the slowdown has not been robustly shown yet.
54 Furthermore, slowing trends have not been unambiguously observed in circulation patterns that steer TCs
55 such as the Walker and Hadley circulations (Section 2.3.1.4), although these circulations generally slow
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1 down in numerical simulations under global warming (Sections 4.5.1.6 and 8.4.2.2).
2
3 The observed poleward trend in western North Pacific TCs remains significant after accounting for the
4 known modes of dominant interannual to decadal variability in the region (Kossin et al., 2016a), and is also
5 found in CMIP5 model-simulated TCs (in the recent historical period 1980–2005), although it is weaker than
6 observed and is not statistically significant (Kossin et al., 2016a). However, the trend is significant in 21st
7 century CMIP5 projections under the RCP8.5 scenario, with a similar spatial pattern and magnitude to the
8 past observed changes in that basin over the period 1945–2016, supporting a possible anthropogenic GHG
9 contribution to the observed trends (Knutson et al., 2019; Kossin et al., 2016a).
10
11 The recent active TC seasons in some basins have been studied to determine whether there is anthropogenic
12 influence. For 2015, Murakami et al. (2017) explored the unusually high TC frequency near Hawaii and in
13 the eastern Pacific basin. Zhang et al. (2016) considered unusually high Accumulated Cyclone Energy
14 (ACE) in the western North Pacific. Yang et al. (2018) and Yamada et al. (2019) looked at TC intensification
15 in the western North Pacific. These studies suggest that the anomalous TC activity in 2015 was not solely
16 explained by the effect of an extreme El Niño (see BOX 11.3), that there was also an anthropogenic
17 contribution, mainly through the effects of SSTs in subtropical regions. In the post-monsoon seasons of 2014
18 and 2015, tropical storms with lifetime maximum winds greater than 46 m s−1 were first observed over the
19 Arabian Sea, and Murakami et al. (2017b) showed that the probability of late-season severe tropical storms is
20 increased by anthropogenic forcing compared to the preindustrial era. Murakami et al. (2018) concluded that
21 the active 2017 Atlantic hurricane season was mainly caused by pronounced SSTs in the tropical North
22 Atlantic and that these types of seasonal events will intensify with projected anthropogenic forcing. The
23 trans-basin SST change, which might be driven by anthropogenic aerosol forcing, also affects TC activity.
24 Takahashi et al. (2017) suggested that a decrease in sulfate aerosol emissions caused about half of the
25 observed decreasing trends in TC genesis frequency in the south-eastern region of the western North Pacific
26 during 1992–2011.
27
28 Event attribution is used in case studies of TCs to test whether the severities of recent intense TCs are
29 explained without anthropogenic effects. In a case study of Hurricane Sandy (2012), Lackmann (2015) found
30 no statistically significant impact of anthropogenic climate change on storm intensity, while projections in a
31 warmer world showed significant strengthening. On the other hand, Magnusson et al. (2014) found that in
32 ECMWF simulations, the simulated cyclone depth and intensity, as well as precipitation, were larger when
33 the model was driven by the warmer actual SSTs than the climatological average SSTs. In super typhoon
34 Haiyan, which struck the Philippines on 8 November 2013, Takayabu et al. (2015) took an event attribution
35 approach with cloud system-resolving (~1km) downscaling ensemble experiments to evaluate the
36 anthropogenic effect on typhoons, and showed that the intensity of the simulated worst case storm in the
37 actual conditions was stronger than that in a hypothetical condition without historical anthropogenic forcing
38 in the model. However, in a similar approach with two coarser parameterized convection models, Wehner et
39 al. (2018) found conflicting human influences on Haiyan’s intensity. Patricola and Wehner (2018) found
40 little evidence of an attributable change in intensity of hurricanes Katrina (2005), Irma (2017), and Maria
41 (2017) using a regional climate model configured between 3 and 4.5 km resolution. They did, however, find
42 attributable increases in heavy precipitation totals. These results imply that higher resolution, such as in a
43 convective permitting 5-km or less mesh model, is required to obtain a robust anthropogenic intensification
44 of a strong TC by simulating realistic rapid intensification of a TC (Kanada and Wada, 2016; Kanada et al.,
45 2017a), and that whether the intensification of TCs can be attributed to the recent warming depends on the
46 case.
47
48 The dominant factor in the extreme rainfall amounts during Hurricane Harvey’s passage onto the U.S. in
49 2017 was its slow translation speed. But studies published after the event have argued that anthropogenic
50 climate change contributed to an increase in rain rate, which compounded the extreme local rainfall caused
51 by the slow translation. Emanuel (2017) used a large set of synthetically-generated storms and concluded
52 that the occurrence of extreme rainfall as observed in Harvey was substantially enhanced by anthropogenic
53 changes to the larger-scale ocean and atmosphere characteristics. Trenberth et al. (2018) linked Harvey’s
54 rainfall totals to the anomalously large ocean heat content from the Gulf of Mexico. van Oldenborgh et al.
55 (2017) and Risser and Wehner (2017) applied extreme value analysis to extreme rainfall records in the
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1 Houston, Texas region and both attributed large increases to climate change. Large precipitation increases
2 during Harvey due to global warming were also found using climate models (van Oldenborgh et al., 2017;
3 Wang et al., 2018b). Harvey precipitation totals were estimated in these papers to be 3 to 10 times more
4 probable due to climate change. A best estimate from a regional climate and flood model is that urbanization
5 increased the risk of the Harvey flooding by a factor of 21 (Zhang et al., 2018c). Anthropogenic effects on
6 precipitation increases were also predicted in advance from a forecast model for Hurricane Florence in 2018
7 (Reed et al., 2020).
8
9 In summary, it is very likely that the recent active TC seasons in the North Atlantic, the North Pacific, and
10 Arabian basins cannot be explained without an anthropogenic influence. The anthropogenic influence on
11 these changes is principally associated to aerosol forcing, with stronger contributions to the response in the
12 North Atlantic. It is more likely than not that the slowdown of TC translation speed over the USA has
13 contributions from anthropogenic forcing. It is likely that the poleward migration of TCs in the western
14 North Pacific and the global increase in TC intensity rates cannot be explained entirely by natural variability.
15 Event attribution studies of specific strong TCs provide limited evidence for anthropogenic effects on TC
16 intensifications so far, but high confidence for increases in TC heavy precipitation. There is high confidence
17 that anthropogenic climate change contributed to extreme rainfall amounts during Hurricane Harvey (2017)
18 and other intense TCs.
19
20
21 11.7.1.5 Projections
22
23 A summary of studies on TC projections for the late 21st century, particularly studies since AR5, is given by
24 Knutson et al. (2020), which is an assessment report mandated by the World Meteorological Organization
25 (WMO). Studies subsequent to Knutson et al. (2020) are generally consistent and the confidence assessments
26 here closely follow theirs (Cha et al., 2020), although there are some differences due to the different
27 confidence calibrations between the IPCC and WMO reports.
28
29 There is not an established theory for the drivers of future changes in the frequency of TCs. Most, but not all,
30 high-resolution global simulations project significant reductions in the total number of TCs, with the bulk of
31 the reduction at the weaker end of the intensity spectrum as the climate warms (Knutson et al., 2020). Recent
32 exceptions based on high-resolution coupled model results are noted in Bhatia et al. (2018) and Vecchi et al.
33 (2019). Vecchi et al. (2019) showed that the representation of synoptic-scale seeds for TC genesis in their
34 high-resolution model causes different projections of global TC frequency, and there is evidence for a
35 decrease in seeds in some projected TC simulations (Sugi et al., 2020). However, other research indicates
36 that TC seeds are not an independent control on climatological TC frequency, rather the seeds covary with
37 the large-scale controls on TCs (Patricola et al., 2018). While empirical genesis indices derived from
38 observations and reanalysis describe well the observed subseasonal and interannual variability of current TC
39 frequency (Camargo et al., 2007, 2009; Tippett et al., 2011; Menkes et al., 2012), they fail to predict the
40 decreased TC frequency found in most high-resolution model simulations (Zhang et al., 2010; Camargo,
41 2013; Wehner et al., 2015), as they generally project an increase as the climate warms. This suggests a
42 limitation of the use of the empirical genesis indices for projections of TC genesis, in particular due to their
43 sensitivity to the humidity variable considered in the genesis index for these projections (Camargo et al.,
44 2014). In a different approach, a statistical-dynamical downscaling framework assuming a constant seeding
45 rate with warming (Emanuel, 2013, 2021) exhibits increases in TC frequency consistent with genesis indices
46 based projections, while downscaling with a different model leads to two different scenarios depending on
47 the humidity variable considered (Lee et al., 2020a). This disparity in the sign of the projected change in
48 global TC frequency and the difficulty in explaining the mechanisms behind the different signed responses
49 further emphasizes the lack of process understanding of future changes in tropical cyclogenesis (Walsh et al.,
50 2015; Hoogewind et al., 2020). Even within a single model, uncertainty in the pattern of future SST changes
51 leads to large uncertainties (including the sign) in the projected change in TC frequency in individual ocean
52 basins, although global TCs would appear to be less sensitive (Yoshida et al., 2017; Bacmeister et al., 2018).
53
54 Changes in SST and atmospheric temperature and moisture play a role in tropical cyclogenesis (Walsh et al.,
55 2015). Reductions in vertical convective mass flux due to increased tropical stability have been associated
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1 with a reduction in cyclogenesis (Held and Zhao, 2011; Sugi et al., 2012). Satoh et al. (2015) further posits
2 that the robust simulated increase in the number of intense TCs, and hence increased vertical mass flux
3 associated with intense TCs, must lead to a decrease in overall TC frequency because of this association. The
4 Genesis Potential Index can be modified to mimic the TC frequency decreases of a model by altering the
5 treatment of humidity (Camargo et al., 2014), supporting the idea that increased mid-tropospheric saturation
6 deficit (Emanuel et al., 2008) controls TC frequency, but the approach remains empirical. Other possible
7 controlling factors, such as a decline in the number of seeds (held constant in Emanuel’s downscaling
8 approach, or dependent on the genesis index formulation in the approach proposed by Lee et al., 2020)
9 caused by increased atmospheric stability have been proposed, but questioned as an important factor
10 (Patricola et al., 2018). The resolution of atmospheric models affects the number of seeds, hence TC genesis
11 frequency (Vecchi et al., 2019; Sugi et al., 2020; Yamada et al., 2021). The diverse and sometimes
12 inconsistent projected changes in global TC frequency by high-resolution models indicate that better process
13 understanding and improvement of the models are needed to raise confidence in these changes.
14
15 Most TC-permitting model simulations (10-60 km or finer grid spacing) are consistent in their projection of
16 increases in the proportion of intense TCs (Category 4-5), as well as an increase in the intensity of the
17 strongest TCs defined by maximum wind speed or central pressure fall (Murakami et al., 2012; Tsuboki et
18 al., 2015; Wehner et al., 2018a; Knutson et al., 2020). The general reduction in the total number of TCs,
19 which is concentrated in storms weaker than or equal to Category 1, contributes to this increase. The models
20 are somewhat less consistent in projecting an increase in the frequency of Category 4-5 TCs (Wehner et al.,
21 2018a). The projected increase in the intensity of the strongest TCs is consistent with theoretical
22 understanding (e.g., Emanuel, 1987) and observations (e.g., Kossin et al., 2020). For a 2°C global warming,
23 the median proportion of Category 4–5 TCs increases by 13%, while the median global TC frequency
24 decreases by 14%, which implies that the median of the global Category 4–5 TC frequency is slightly
25 reduced by 1% or almost unchanged (Knutson et al., 2020). Murakami et al. (2020) projected a decrease in
26 TC frequency over the coming century in the North Atlantic due to greenhouse warming, as consistent with
27 Dunstone et al. (2013), and a reduction in TC frequency almost everywhere in the tropics in response to +1%
28 CO2 forcing; exceptions include the central North Pacific (Hawaii region), east of the Philippines in the
29 North Pacific, and two relatively small regions in the northern Arabian Sea and Bay of Bengal. These
30 projections can vary substantially between ocean basins, possibly due to differences in regional SST
31 warming and warming patterns (Sugi et al., 2017; Yoshida et al., 2017; Bacmeister et al., 2018). A summary
32 of projections of TC characteristics is schematically shown by Figure 11.20.
33
34 The increase in global TC maximum surface wind speeds is about 5% for a 2°C global warming across a
35 number of high-resolution multi-decadal studies (Knutson et al., 2020). This indicates the deepening in
36 global TC minimum surface pressure under the global warming conditions. A regional cloud-permitting
37 model study shows that the strongest TC in the western North Pacific can be as strong as 857 hPa in
38 minimum surface pressure with a wind speed of 88 m s-1 under warming conditions in 2074-2087 (Tsuboki
39 et al., 2015). TCs are also measured by quantities such as Accumulated Cyclone Energy (ACE) and the
40 power dissipation index (PDI), which conflate TC intensity, frequency, and duration (Murakami et al., 2014).
41 Several TC modeling studies (Yamada et al., 2010; Kim et al., 2014a; Knutson et al., 2015) project little
42 change or decreases in the globally-accumulated value of PDI or ACE, which is due to the decrease in the
43 total number of TCs.
44
45 A projected increase in global average TC rainfall rates of about 12% for a 2°C global warming is consistent
46 with the Clausius-Clapeyron scaling of saturation specific humidity (Knutson et al., 2020). Increases
47 substantially greater than Clausius-Clapeyron scaling are projected in some regions, which is caused by
48 increased low-level moisture convergence due to projected TC intensity increases in those regions (Knutson
49 et al., 2015; Phibbs and Toumi, 2016; Patricola and Wehner, 2018; Liu et al., 2019c). Projections of TC
50 precipitation using large-ensemble experiments (Kitoh and Endo, 2019) show that the annual maximum 1-
51 day precipitation total is projected to increase, except for the western North Pacific where there is only a
52 small change or even a reduction is projected, mainly due to a projected decrease of TC frequency. They also
53 show that the 10-year return value of extreme Rx1day associated with TCs will greatly increase in a region
54 extending from Hawaii to the south of Japan. TC tracks and the location of topography relative to TCs
55 significantly affect precipitation, thus in general, areas on the eastern and southern faces of mountains have
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1 more impacts of TC precipitation changes (Hatsuzuka et al., 2020). Projection studies using variable-
2 resolution models in the North Atlantic (Stansfield et al., 2020) indicate that TC-related precipitation rates
3 within North Atlantic TCs and the amount of hourly precipitation due to TC are projected to increase by the
4 end of the century compared to a historical simulation. However, the annual average TC-related Rx5day over
5 the eastern United States is projected to decrease because of a decrease in landfalling TCs. RCM studies with
6 around 25-50 km grid spacing are used to study projected changes in TCs. The projected changes of TCs in
7 Southeast Asia simulated by RCMs are consistent with those of most global climate models, showing a
8 decrease in TC frequency and an increase in the amount of TC-associated precipitation or an increase in the
9 frequency of intense TCs (Redmond et al., 2015; Gallo et al., 2019).
10
11 Projected changes in TC tracks or TC areas of occurrence in the late 21st century vary considerably among
12 available studies, although there is better agreement in the western North Pacific. Several studies project
13 either poleward or eastward expansion of TC occurrence over the western North Pacific region, and more TC
14 occurrence in the central North Pacific (Yamada et al., 2017; Yoshida et al., 2017; Wehner et al., 2018a;
15 Roberts et al., 2020b). The observed poleward expansion of the latitude of maximum TC intensity in the
16 western North Pacific is consistently reproduced by the CMIP5 models and downscaled models and these
17 models show further poleward expansion in the future; the projected mean migration rate of the mean
18 latitude where TCs reach their lifetime-maximum intensity is 0.2±0.1° from CMIP5 model results, while it is
19 0.13±0.04° from downscaled models in the western North Pacific (Kossin et al., 2014, 2016a). In the North
20 Atlantic, while the location of TC maximum intensity does not show clear poleward migration
21 observationally (Kossin et al., 2014), it tends to migrate poleward in projections (Garner et al., 2017). The
22 poleward migration is less robust among models and observations in the Indian Ocean, eastern North Pacific,
23 and South Pacific (e.g., Tauvale and Tsuboki, 2019; Ramsay et al. 2018; Cattiaux et al. 2020). There is
24 presently no clear consensus in projected changes in TC translation speed (Knutson et al., 2020), although
25 recent studies suggest a slowdown outside of the tropics (Kossin, 2019; Yamaguchi et al., 2020; Zhang et al.,
26 2020a), but regionally there can even be an acceleration of the storms (Hassanzadeh et al., 2020).
27
28 The spatial extent, or “size”, of the TC wind-field is an important determinant of storm surge and damage.
29 No detectable anthropogenic influences on TC size have been identified to date, because TCs in observations
30 vary in size substantially (Chan and Chan, 2015) and there is no definite theory on what controls TC size,
31 although this is an area of active research (Chavas and Emanuel, 2014; Chan and Chan, 2018). However,
32 projections by high-resolution models indicate future broadening of TC wind-fields when compared to TCs
33 of the same categories (Yamada et al., 2017), while Knutson et al. (2015) simulates a reasonable interbasin
34 distribution of TC size climatology, but projects no statistically significant change in global average TC size.
35 A plausible mechanism is that as the tropopause height becomes higher with global warming, the eye wall
36 areas become wider because the eye walls are inclined outward with height to the tropopause. This effect is
37 only reproduced in high-resolution convection-permitting models capturing eye walls, and such modeling
38 studies are not common. Moreover, the projected TC size changes are generally on the order of 10% or less,
39 and these size changes are still highly variable between basins and studies. Thus, the projected change in
40 both magnitude and sign of TC size is uncertain.
41
42 The coastal effects of TCs depend on TC intensity, size, track, and translation speed. Projected increases in
43 sea level, average TC intensity, and TC rainfall rates each generally act to further elevate future storm surge
44 and fresh-water flooding (see Section 9.6.4.2). Changes in TC frequency could contribute toward increasing
45 or decreasing future storm surge risk, depending on the net effects of changes in weaker vs stronger storms.
46 Several studies (McInnes et al., 2014, 2016; Little et al., 2015; Garner et al., 2017; Timmermans et al., 2017,
47 2018) have explored future projections of storm surge in the context of anthropogenic climate change with
48 the influence of both sea level rise and future TC changes. Garner et al. (2017) investigated the near future
49 changes in the New York City coastal flood hazard, and suggested a small change in storm-surge height
50 because effects of TC intensification are compensated by the offshore shifts in TC tracks, but concluded that
51 the overall effect due to the rising sea levels would increase the flood hazard. Future projection studies of
52 storm surge in East Asia, including China, Japan and Korea, also indicate that storm surge due to TCs
53 become more severe (Yang et al., 2018b; Mori et al., 2019, 2021; Chen et al., 2020c). For the Pacific islands,
54 McInnes et al. (2014) found that the future projected increase in storm surge in Fiji is dominated by sea level
55 rise, and projected TC changes make only a minor contribution. Among various storm surge factors, there is
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1 high confidence that sea level rise will lead to a higher possibility of extreme coastal water levels in most
2 regions, with all other factors assumed equal.
3
4 In the North Atlantic, vertical wind shear, which inhibits TC genesis and intensification, varies in a quasi-
5 dipole pattern with one center of action in the tropics and another along the U.S. southeast coast (Vimont and
6 Kossin, 2007). This pattern of variability creates a protective barrier of high shear along the U.S. coast
7 during periods of heightened TC activity in the tropics (Kossin, 2017), and appears to be a natural part of the
8 Atlantic ocean-atmosphere climate system (Ting et al., 2019). Greenhouse gas forcing in CMIP5 and the
9 Community Earth System Model Large Ensemble (CESM-LE; Kay et al., 2015) simulations, however,
10 erodes the pattern and degrades the natural shear barrier along the U.S. coast. Following the Representative
11 Concentration Pathway 8.5 (RCP8.5) emission scenario, the magnitude of the erosion of the barrier equals
12 the amplitude of past natural variability (time of emergence) by the mid-twenty-first century (Ting et al.,
13 2019). The projected reduction of shear along the U.S. East Coast with warming is consistent among studies
14 (e.g., Vecchi and Soden, 2007).
15
16 In summary, average peak TC wind speeds and the proportion of Category 4-5 TCs will very likely increase
17 globally with warming. It is likely that the frequency of Category 4-5 TCs will increase in limited regions
18 over the western North Pacific. It is very likely that average TC rain-rates will increase with warming, and
19 likely that the peak rain-rates will increase at greater than the Clausius-Clapeyron scaling rate of 7% per °C
20 of warming in some regions due to increased low-level moisture convergence caused by regional increases in
21 TC wind-intensity. It is likely that the average location where TCs reach their peak wind-intensity will
22 migrate poleward in the western North Pacific Ocean as the tropics expand with warming, and that the global
23 frequency of TCs over all categories will decrease or remain unchanged.
24
25
26 11.7.2 Extratropical storms
27
28 This section focuses on extratropical cyclones (ETCs) that are either classified as strong or extreme by using
29 some measure of their intensity, or by being associated with the occurrence of extremes in variables such as
30 precipitation or near-surface wind speed (Seneviratne et al., 2012). Since AR5, the high relevance of ETCs
31 for extreme precipitation events has been well established (Pfahl and Wernli, 2012; Catto and Pfahl, 2013;
32 Utsumi et al., 2017), with 80% or more of hourly and daily precipitation extremes being associated with
33 either ETCs or fronts over oceanic mid-latitude regions, and somewhat smaller, but still very large,
34 proportions of events over mid-latitude land regions (Utsumi et al., 2017). The emphasis in this section is on
35 individual ETCs that have been identified using some detection and tracking algorithms. Mid-latitude
36 atmospheric rivers are assessed in Section 8.3.2.8.
37
38
39 11.7.2.1 Observed trends
40
41 Chapter 2 (Section 2.3.1.4.3) concluded that there is overall low confidence in recent changes in the total
42 number of ETCs over both hemispheres and that there is medium confidence in a poleward shift of the storm
43 tracks over both hemispheres since the 1980s. Overall, there is also low confidence in past-century trends in
44 the number and intensity of the strongest ETCs due to the large interannual and decadal variability (Feser et
45 al., 2015; Reboita et al., 2015; Wang et al., 2016; Varino et al., 2018) and due to temporal and spatial
46 heterogeneities in the number and type of assimilated data in reanalyses, particularly before the satellite era
47 (Krueger et al., 2013; Tilinina et al., 2013; Befort et al., 2016; Chang and Yau, 2016; Wang et al., 2016).
48 There is medium confidence that the agreement among reanalyses and among detection and tracking
49 algorithms is higher when considering stronger cyclones (Neu et al., 2013; Pepler et al., 2015; Wang et al.,
50 2016). Over the Southern Hemisphere, there is high confidence that the total number of ETCs with low
51 central pressures (<980 hPa) has increased between 1979 and 2009, with all eight reanalyses considered by
52 Wang et al. (2016), showing positive trends and five of them showing statistically significant trends. Similar
53 results were found by (Reboita et al., 2015) using a different detection and tracking algorithm and a single
54 reanalysis product. Over the Northern Hemisphere, there is high agreement among reanalyses that the
55 number of cyclones with low central pressures (<970 hPa) has decreased in summer and winter during the
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1 period 1979-2010 (Tilinina et al., 2013; Chang et al., 2016). However, changes exhibit substantial decadal
2 variability and do not show monotonic trends since the 1980s. For example, over the Arctic and North
3 Atlantic, Tilinina et al. (2013) showed the number of cyclones with very low central pressure (<960 hPa)
4 increased from 1979 to 1990 and then declined until 2010 in all five reanalyses considered. Over the North
5 Pacific, the number of cyclones with very low central pressure reached a peak around 2000 and then
6 decreased until 2010 in the five reanalyses considered (Tilinina et al., 2013).
7
8 Overall, however, it should be noted that characterising trends in the dynamical intensity of ETCs (e.g., wind
9 speeds) using the absolute central pressure is problematic because the central pressure depends on the
10 background mean sea level pressure, which varies seasonally and regionally (e.g., Befort et al., 2016).
11
12
13 11.7.2.2 Model evaluation
14
15 There is high confidence that coarse-resolution climate models (e.g., CMIP5 and CMIP6) underestimate the
16 dynamical intensity of ETCs, including the strongest ETCs, as measured using a variety of metrics, including
17 mean pressure gradient, mean vorticity and near-surface winds, over most regions (Colle et al., 2013; Zappa
18 et al., 2013a; Govekar et al., 2014; Di Luca et al., 2016; Trzeciak et al., 2016; Seiler et al., 2018; Priestley et
19 al., 2020). There is also high confidence that most current climate models underestimate the number of
20 explosive systems (i.e., systems showing a decrease in mean sea level pressure of at least 24 hPa in 24 hours)
21 over both hemispheres (Seiler and Zwiers, 2016a; Gao et al., 2020; Priestley et al., 2020). There is high
22 confidence that the underestimation of the intensity of ETCs is associated with the coarse horizontal
23 resolution of climate models, with higher horizontal resolution models, including models from HighResMIP
24 and CORDEX, usually showing better performance (Colle et al., 2013; Zappa et al., 2013a; Di Luca et al.,
25 2016; Trzeciak et al., 2016; Seiler et al., 2018; Gao et al., 2020; Priestley et al., 2020). The improvement by
26 higher-resolution models is found even when comparing models and reanalyses after postprocessing data to a
27 common resolution (Zappa et al., 2013a; Di Luca et al., 2016; Priestley et al., 2020). The systematic bias in
28 the intensity of ETCs has also been linked to the inability of current climate models to well resolve diabatic
29 processes, particularly those related to the release of latent heat (Willison et al., 2013; Trzeciak et al., 2016)
30 and the formation of clouds (Govekar et al., 2014). There is medium confidence that climate models simulate
31 well the spatial distribution of precipitation associated with ETCs over the Northern Hemisphere, together
32 with some of the main features of the ETC life cycle, including the maximum in precipitation occurring just
33 before the peak in dynamical intensity (e.g., vorticity) as observed in a reanalysis and observations
34 (Hawcroft et al., 2018). There is, however, large observational uncertainty in ETC-associated precipitation
35 (Hawcroft et al., 2018) and limitations in the simulation of frontal precipitation, including too low rainfall
36 intensity over mid-latitude oceanic areas in both hemispheres (Catto et al., 2015).
37
38
39 11.7.2.3 Detection and attribution, event attribution
40
41 Chapter 3 (Section 3.3.3.3) concluded that there is low confidence in the attribution of observed changes in
42 the number of ETCs in the Northern Hemisphere and that there is high confidence that the poleward shift of
43 storm tracks in the Southern Hemisphere is linked to human activity, mostly due to emissions of ozone-
44 depleting substances. Specific studies attributing changes in the most extreme ETCs are not available. The
45 human influence on individual extreme ETC events has been considered only a few times and there is overall
46 low confidence in the attribution of these changes (NASEM, 2016; Vautard et al., 2019).
47
48
49 11.7.2.4 Projections
50
51 The frequency of ETCs is expected to change primarily following a poleward shift of the storm tracks as
52 discussed in Chapters 4 (Section 4.5.1.6, see also Figure 4.31) and 8 (Section 8.4.2.8). There is medium
53 confidence that changes in the dynamical intensity (e.g., wind speeds) of ETCs will be small, although
54 changes in the location of storm tracks can lead to substantial changes in local extreme wind speeds (Zappa
55 et al., 2013b; Chang, 2014; Li et al., 2014; Seiler and Zwiers, 2016b; Yettella and Kay, 2017; Barcikowska
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1 et al., 2018; Kar-Man Chang, 2018). Yettella and Kay (2017) detected and tracked ETCs over both
2 hemispheres in an ensemble of 30 CESM-LE simulations, differing only in their initial conditions, and found
3 that changes in mean wind speeds around ETC centres are often negligible between present (1986-2005) and
4 future (2081-2100) periods. Using 19 CMIP5 models, Zappa et al. (2013b) found an overall reduction in the
5 number of cyclones associated with low-troposphere (850-hPa) wind speeds larger than 25 m s-1 over the
6 North Atlantic and Europe with the number of the 10% strongest cyclones decreasing by about 8% and 6%
7 in DJF and JJA according to the RCP4.5 scenario (2070-2099 vs. 1976-2005). Over the North Pacific, Chang
8 (2014) showed that CMIP5 models project a decrease in the frequency of ETCs with the largest central
9 pressure perturbation (i.e., the depth, strongly related with low-level wind speeds) by the end of the century
10 according to simulations using the RCP8.5 scenario. Using projections from CMIP5 GCMs under the
11 RCP8.5 scenario (1981-2000 to 2081-2100), Seiler and Zwiers (2016b) projected a northward shift in the
12 number of explosive ETCs in the northern Pacific, with fewer and weaker events south, and more frequent
13 and stronger events north of 45°N. Using 19 CMIP5 GCMs under the RCP8.5 scenario, (Kar-Man Chang,
14 2018) found a significant decrease in the number of ETCs associated with extreme wind speeds (2081–2100
15 vs. 1980–99) over the Northern Hemisphere (average decrease of 17%) and over some smaller regions,
16 including the Pacific and Atlantic regions.
17
18 Over the Southern Hemisphere, future changes (RCP8.5 scenario; 1980-1999 to 2081-2100) in extreme
19 ETCs were studied by Chang (2017) using 26 CMIP5 models and a variety of intensity metrics (850-hPa
20 vorticity, 850-hPa wind speed, mean sea level pressure and near-surface wind speed). They found that the
21 number of extreme cyclones is projected to increase by at least 20% and as much as 50%, depending on the
22 specific metric used to define extreme ETCs. Increases in the number of strong cyclones appear to be robust
23 across models and for most seasons, although they show strong regional variations with increases occurring
24 mostly over the southern flank of the storm track, consistent with a shift and intensification of the storm
25 track. Overall, there is medium confidence that projected changes in the dynamical intensity of ETCs depend
26 on the resolution and formulation (e.g., explicit or implicit representation of convection) of climate models
27 (Booth et al., 2013; Michaelis et al., 2017; Zhang and Colle, 2017).
28
29 As reported in AR5 and in Chapter 8 (Section 8.4.2.8), despite small changes in the dynamical intensity of
30 ETCs, there is high confidence that the precipitation associated with ETCs will increase in the future (Zappa
31 et al., 2013b; Marciano et al., 2015; Pepler et al., 2016; Zhang and Colle, 2017; Michaelis et al., 2017;
32 Yettella and Kay, 2017; Barcikowska et al., 2018; Zarzycki, 2018; Hawcroft et al., 2018; Kodama et al.,
33 2019; Bevacqua et al., 2020c; Reboita et al., 2020). There is high confidence that increases in precipitation
34 will follow increases in low-level water vapour (i.e., about 7% per degree of surface warming; Box 11.1) and
35 will be largest for higher warming levels (Zhang and Colle, 2017). There is medium confidence that
36 precipitation changes will show regional and seasonal differences due to distinct changes in atmospheric
37 humidity and dynamical conditions (Zappa et al., 2015; Hawcroft et al., 2018), with even decreases in some
38 specific regions such as the Mediterranean (Zappa et al., 2015; Barcikowska et al., 2018). There is high
39 confidence that snowfall associated with wintertime ETCs will decrease in the future, because increases in
40 tropospheric temperatures lead to a lower proportion of precipitation falling as snow (O’Gorman, 2014;
41 Rhoades et al., 2018; Zarzycki, 2018). However, there is medium confidence that extreme snowfall events
42 associated with wintertime ETCs will change little in regions where snowfall will be supported in the future
43 (O’Gorman, 2014; Zarzycki, 2018).
44
45 In summary, there is low confidence in past changes in the dynamical intensity (e.g., maximum wind speeds)
46 of ETCs and medium confidence that in the future these changes will be small, although changes in the
47 location of storm tracks could lead to substantial changes in local extreme wind speeds. There is high
48 confidence that average and maximum ETC precipitation-rates will increase with warming, with the
49 magnitude of the increases associated with increases in atmospheric water vapour. There is medium
50 confidence that projected changes in the intensity of ETCs, including wind speeds and precipitation, depend
51 on the resolution and formulation of climate models.
52
53
54 11.7.3 Severe convective storms
55
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1 Severe convective storms are convective systems that are associated with extreme phenomena such as
2 tornadoes, hail, heavy precipitation (rain or snow), strong winds, and lightning. The assessment of changes in
3 severe convective storms in SREX (Chapter 3, Seneviratne et al., 2012) and AR5 (Chapter 12, Collins et al.,
4 2013) is limited and focused mainly on tornadoes and hail storms. SREX assessed that there is low
5 confidence in observed trends in tornadoes and hail because of data inhomogeneities and inadequacies in
6 monitoring systems. Subsequent literature assessed in the Climate Science Special Report (Kossin et al.,
7 2017) led to the assessment of the observed tornado activity over the 2000s in the United States with a
8 decrease in the number of days per year with tornadoes and an increase in the number of tornadoes on these
9 days (medium confidence). However, there is low confidence in past trends for hail and severe thunderstorm
10 winds. Climate models consistently project environmental changes that would support an increase in the
11 frequency and intensity of severe thunderstorms that combine tornadoes, hail, and winds (high confidence),
12 but there is low confidence in the details of the projected increase. Regional aspects of severe convective
13 storms and details of the assessment of tornadoes and hail are also assessed in Chapter 12 (Section 12.3.3.2
14 for tornadoes; Section 12.3.4.5 for hail; Section 12.4.5.3 for Europe, Section 12.4.6.3 for North America, and
15 Section 12.7.2 for regional gaps and uncertainties).
16
17
18 11.7.3.1 Mechanisms and drivers
19
20 Severe convective storms are sometimes embedded in synoptic-scale weather systems, such as TCs, ETCs,
21 and fronts (Kunkel et al., 2013). They are also generated as individual events as mesoscale convective
22 systems (MCSs) and mesoscale convective complexes (MCCs) (a special type of a large,organized and long-
23 lived MCS), without being clearly embedded within larger-scale weather systems. In addition to the general
24 vigorousness of precipitation, hail, and winds associated with MCSs, characteristics of MCSs are viewed in
25 new perspectives in recent years, probably because of both the development of dense mesoscale observing
26 networks and advances in high-resolution mesoscale modelling (Sections 11.7.3.2 and 11.7.3.3). The
27 horizontal scale of MCSs is discussed with their organization of the convective structure and it is examined
28 with a concept of "convective aggregation" in recent years (Holloway et al., 2017). MCSs sometimes take a
29 linear shape and stay almost stationary with successive production of cumulonimbus on the upstream side
30 (back-building type convection), and cause heavy rainfall (Schumacher and Johnson, 2005). Many of the
31 recent severe rainfall events in Japan are associated with band-shaped precipitation systems (Kunii et al.,
32 2016; Oizumi et al., 2018; Tsuguti et al., 2018; Kato, 2020), suggesting common characteristics of severe
33 precipitation, at least in East Asia. The convective modes of severe storms in the United States can be
34 classified into rotating or linear modes and preferable environmental conditions for these modes, such as
35 vertical shear, have been identified (Trapp et al., 2005; Smith et al., 2013; Allen, 2018). Cloud microphysics
36 characteristics of MCSs were examined and the roles of warm rain processes on extreme precipitation were
37 emphasized recently (Sohn et al., 2013; Hamada et al., 2015; Hamada and Takayabu, 2018). Idealized
38 studies also suggest the importance of ice and mixed-phase processes of cloud microphysics on extreme
39 precipitation (Sandvik et al., 2018; Bao and Sherwood, 2019). However, it is unknown whether the types of
40 MCSs are changing in recent periods or observed ubiquitously all over the world.
41
42 Severe convective storms occur under conditions preferable for deep convection, that is, conditionally
43 unstable stratification, sufficient moisture both in lower and middle levels of the atmosphere, and a strong
44 vertical shear. These large-scale environmental conditions are viewed as necessary conditions for the
45 occurrence of severe convective systems, or the resulting tornadoes and lightning, and the relevance of these
46 factors strongly depends on the region (e.g., Antonescu et al., 2016a; Allen, 2018; Tochimoto and Niino,
47 2018). Frequently used metrics are atmospheric static stability, moisture content, convective available
48 potential energy (CAPE) and convective inhibition (CIN), wind shear or helicity, including storm-relative
49 environmental helicity (SREH) (Tochimoto and Niino, 2018; Elsner et al., 2019). These metrics, largely
50 controlled by large-scale atmospheric circulations or synoptic weather systems, such as TCs and ETCs, are
51 then generally used to examine severe convective systems. In particular, there is high confidence that CAPE
52 in the tropics and the subtropics increases in response to global warming (Singh et al., 2017a), as supported
53 by theoretical studies (Singh and O’Gorman, 2013; Seeley and Romps, 2015; Romps, 2016; Agard and
54 Emanuel, 2017). The uncertainty, however, arises from the balance between factors affecting severe storm
55 occurrence. For example, the warming of mid-tropospheric temperatures leads to an increase in the freezing
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1 level, which leads to increased melting of smaller hailstones, while there may be some offset by stronger
2 updrafts driven by increasing CAPE, which would favour the growth of larger hailstones, leading to less
3 melting when falling (Allen, 2018; Mahoney, 2020).
4
5 There are few studies on relations between changes in severe convective storms and those of the large-scale
6 circulation patterns. Tornado outbreaks in the United States are usually associated with ETCs with their
7 frontal systems and TCs (Fuhrmann et al., 2014; Tochimoto and Niino, 2016). In early June in East Asia,
8 associated with the Baiu/Changma/Mei-yu, severe precipitation events are frequently caused by MCSs.
9 Severe precipitation events are also caused by remote effects of TCs, known as predecessor rain events
10 (PREs) (Galarneau et al., 2010). Atmospheric rivers and other coherent types of enhanced water vapour flux
11 also have the potential to induce severe convective systems (Kamae et al., 2017; Ralph et al., 2018; Waliser
12 and Guan, 2017; see Section 8.3.2.8.1). Combined with the above drivers, topographic effects also enhance
13 the intensity and duration of severe convective systems and the associated precipitation (Ducrocq et al.,
14 2008; Piaget et al., 2015). However, the changes in these drivers are not generally significant, so their
15 relations to severe convective storms are unclear.
16
17 In summary, severe convective storms are sometimes embedded in synoptic-scale weather systems, such as
18 TCs, ETCs, and fronts, and modulated by large-scale atmospheric circulation patterns. The occurrence of
19 severe convective storms and the associated severe events, including tornadoes, hail, and lightning, is
20 affected by environmental conditions of the atmosphere, such as CAPE and vertical shear. The uncertainty,
21 however, arises from the balance between these environmental factors affecting severe storm occurrence.
22
23
24 11.7.3.2 Observed trends
25
26 Observed trends in severe convective storms or MCSs are not well documented, but the climatology of
27 MCSs has been analysed in specific regions (North America, South America, Europe, Asia; regional aspects
28 of convective storms are separately assessed in Chapter 12). As the definition of severe convective storms
29 varies depending on the literature, it is not straightforward to make a synthesizing view of observed trends in
30 severe convective storms in different regions. However, analysis using satellite observations provides a
31 global view of MCSs (Kossin et al., 2017). The global distribution of thunderstorms is captured (Zipser et
32 al., 2006; Liu and Zipser, 2015) by using the satellite precipitation measurements by the Tropical Rainfall
33 Measuring Mission (TRMM) and Global Precipitation Mission (GPM) (Hou et al., 2014). The climatological
34 characteristics of MCSs are provided by satellite analyses in South America (Durkee and Mote, 2010;
35 Rasmussen and Houze, 2011; Rehbein et al., 2018) and those of MCC in the Maritime Continent by
36 Trismidianto and Satyawardhana (2018). Analysis of the environmental conditions favourable for severe
37 convective events indirectly indicates the climatology and trends of severe convective events (Allen et al.,
38 2018; Taszarek et al., 2018, 2019), though favourable conditions depend on the location, such as the
39 difference for tornadoes associated with ETCs between the United States and Japan (Tochimoto and Niino,
40 2018).
41
42 Observed trends in severe convective storms are highly regionally dependent. In the United States, it is
43 indicated that there is no significant increase in convective storms, and hail and severe thunderstorms
44 (Kossin et al., 2017; Kunkel et al., 2013). There is an upward trend in the frequency and intensity of extreme
45 precipitation events in the United States (high confidence) (Kunkel et al., 2013; Easterling et al., 2017), and
46 MCSs have increased in occurrence and precipitation amounts since 1979 (limited evidence) (Feng et al.,
47 2016). Significant interannual variability of hailstone occurrences is found in the Southern Great Plains of
48 the United States (Jeong et al., 2020). The mean annual number of tornadoes has remained relatively
49 constant, but their variability of occurrence has increased since the 1970s, particularly over the 2000s, with a
50 decrease in the number of days per year, but an increase in the number of tornadoes on these days (Brooks et
51 al., 2014; Elsner et al., 2015, 2019; Kossin et al., 2017; Allen, 2018). There has been a shift in the
52 distribution of tornadoes, with increases in tornado occurrence in the mid-south of the US and decreases over
53 the High Plains (Gensini and Brooks, 2018). Trends in MCSs are relatively more visible for particular
54 aspects of MCSs, such as lengthening of active seasons and dependency on duration. MCSs have increased
55 in occurrence and precipitation amounts since 1979 (Easterling et al., 2017). Feng et al. (2016) analysed that
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1 the observed increases in springtime total and extreme rainfall in the central United States are dominated by
2 MCSs, with increased frequency and intensity of long-lasting MCSs.
3
4 Studies on trends in severe convective storms and their ingredients outside of the United States are limited.
5 Westra et al. (2014) found that there is an increase in the intensity of short-duration convective events
6 (minutes to hours) over many regions of the world, except eastern China. In Europe, a climatology of
7 tornadoes shows an increase in detected tornadoes between 1800 to 2014, but this trend might be affected by
8 the density of observations (Antonescu et al., 2016b, 2016a). An increase in the trend in extreme daily
9 rainfall is found in southeastern France, where MCSs play a key role in this type of event (Blanchet et al.,
10 2018; Ribes et al., 2019). Trend analysis of the mean annual number of days with thunderstorms since 1979
11 in Europe indicates an increase over the Alps and central, southeastern, and eastern Europe, with a decrease
12 over the southwest (Taszarek et al., 2019). In the Sahelian region, Taylor et al. (2017) analysed MCSs using
13 satellite observations since 1982 and showed an increase in the frequency of extreme storms. In Bangladesh,
14 the annual number of propagating MCSs decreased significantly during 1998-2015 based on TRMM
15 precipitation data (Habib et al., 2019). Prein and Holland (2018) estimated the hail hazard from large-scale
16 environmental conditions using a statistical approach and showed increasing trends in the United States,
17 Europe, and Australia. However, trends in hail on regional scales are difficult to validate because of an
18 insufficient length of observations and inhomogeneous records (Allen, 2018). The high spatial variability of
19 hail suggests it is reasonable that there would be local signals of both positive and negative trends and the
20 trends that are occurring in hail globally are uncertain. In China, the total number of days that have either a
21 thunderstorm or hail have decreased by about 50% from 1961 to 2010, and the reduction in these severe
22 weather occurrences correlates strongly with the weakening of the East Asian summer monsoon (Zhang et
23 al., 2017b). More regional aspects of severe convective storms are detailed in Chapter 12.
24
25 In summary, because the definition of severe convective storms varies depending on the literature and the
26 region, it is not straightforward to make a synthesizing view of observed trends in severe convective storms
27 in different regions. In particular, observational trends in tornadoes, hail, and lightning associated with
28 severe convective storms are not robustly detected due to insufficient coverage of the long-term
29 observations. There is medium confidence that the mean annual number of tornadoes in the United States has
30 remained relatively constant, but their variability of occurrence has increased since the 1970s, particularly
31 over the 2000s, with a decrease in the number of days per year and an increase in the number of tornadoes on
32 these days (high confidence). Detected tornadoes have also increased in Europe, but the trend depends on the
33 density of observations.
34
35
36 11.7.3.3 Model evaluation
37
38 The explicit representation of severe convective storms requires non-hydrostatic models with horizontal grid
39 spacings below 5 km, denoted as convection-permitting models or storm-resolving models (Section 10.3.1).
40 Convection-permitting models are becoming available to run over a wide domain, such as a continental scale
41 or even over the global area, and show realistic climatological characteristics of MCSs (Prein et al., 2015;
42 Guichard and Couvreux, 2017; Satoh et al., 2019). Such high-resolution simulations are computationally too
43 expensive to perform at the larger domain and for long periods and alternative methods by using an RCM
44 with dynamical downscaling are generally used (Section 10.3.1). Convection-permitting models are used as
45 the flagship project of CORDEX to particularly study projections of thunderstorms (Section 10.3.3).
46 Simulations of North American MCSs by a convection-permitting model conducted by Prein et al. (2017a)
47 were able to capture the main characteristics of the observed MCSs, such as their size, precipitation rate,
48 propagation speed, and lifetime. Cloud-permitting model simulations in Europe also showed sub-daily
49 precipitation realistically (Ban et al., 2014; Kendon et al., 2014). Evaluation of precipitation conducted using
50 convection-permitting simulations around Japan showed that finer resolution improves intense precipitation
51 (Murata et al., 2017). MCSs over Africa simulated using convection-permitting models showed better
52 extreme rainfall (Kendon et al., 2019) and diurnal cycles and convective rainfall over land than the coarser-
53 resolution RCMs or GCMs (Stratton et al., 2018; Crook et al., 2019).
54
55 The other modeling approach is the analysis of the environmental conditions that control characteristics of
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1 severe convective storms using the typical climate model results in CMIP5/6 (Allen, 2018). Severe
2 convective storms are generally formed in environments with large CAPE and tornadic storms are, in
3 particular, formed with a combination of large CAPE and strong vertical wind shear. As the processes
4 associated with severe convective storms occur over a wide range of spatial and temporal scales, some of
5 which are poorly understood and are inadequately sampled by observational networks, the model calibration
6 approaches are in general difficult and insufficiently validated. Therefore, model simulations and their
7 interpretations should be done with much caution.
8
9 In summary, there are typically two kinds of modeling approaches for studying changes in severe convective
10 storms. One is to use convection-permitting models in wider regions or the global domain in time-sliced
11 downscaling methods to directly simulate severe convective storms. The other is the analysis of the
12 environmental conditions that control characteristics of severe convective storms by using coarse-resolution
13 GCMs. Even in finer-resolution convection-permitting models, it is difficult to directly simulate tornadoes,
14 hail storms, and lightning, so modeling studies of these changes are limited.
15
16
17 11.7.3.4 Detection and attribution, event attribution
18
19 It is extremely difficult to detect differences in time and space of severe convective storms (Kunkel et al.,
20 2013). Although some ingredients that are favourable for severe thunderstorms have increased over the
21 years, others have not; thus, overall, changes in the frequency of environments favourable for severe
22 thunderstorms have not been statistically significant. Event attribution studies on severe convective events
23 have now been undertaken for some cases. For the case of the July 2018 heavy rainfall event in Japan (BOX
24 11.3), Kawase et al. (2019) took a storyline approach to show that the rainfall during this event in Japan was
25 increased by approximately 7% due to the recent rapid warming around Japan. For the case of the December
26 2015 extreme rainfall event in Chennai, India, the extremity of the event was equally caused by the warming
27 trend in the Bay of Bengal SSTs and the strong El Niño conditions (van Oldenborgh et al., 2016; Boyaj et al.,
28 2018). For hailstorms, such as those that caused disasters in the United States in 2018, detection of the role
29 of climate change in changing hail storms is more difficult, because hail storms are not, in general, directly
30 simulated by convection-permitting models and not adequately represented by the environmental parameters
31 of coarse-resolution GCMs (Mahoney, 2020).
32
33 In summary, it is extremely difficult to detect and attribute changes in severe convective storms, except for
34 case study approaches by event attribution. There is limited evidence that extreme precipitation associated
35 with severe convective storms has increased in some cases.
36
37
38 11.7.3.5 Projections
39
40 Future projections of severe convective storms are usually studied either by analysing the environmental
41 conditions simulated by climate models or by a time slice approach with higher-resolution convection-
42 permitting models by comparing simulations downscaled with climate model results under historical
43 conditions and those under hypothesized future conditions (Kendon et al., 2017; Allen, 2018). Up to now,
44 individual studies using convection-permitting models gave projections of extreme events associated with
45 severe convective storms in local regions, and it is not generally possible to obtain global or general views of
46 projected changes of severe convective storms. Prein et al. (2017b) investigated future projections of North
47 American MCS simulations and showed an increase in MCS frequency and an increase in total MCS
48 precipitation volume by the combined effect of increases in maximum precipitation rates associated with
49 MCSs and increases in their size. Rasmussen et al. (2017) investigated future changes in the diurnal cycle of
50 precipitation by capturing organized and propagating convection and showed that weak to moderate
51 convection will decrease and strong convection will increase in frequency in the future. Ban et al. (2015)
52 found the day-long and hour-long precipitation events in summer intensify in the European region covering
53 the Alps. Kendon et al. (2019) showed future increases in extreme 3-hourly precipitation in Africa. Murata et
54 al. (2015) investigated future projections of precipitation around Japan and showed a decrease in monthly
55 mean precipitation in the eastern Japan Sea region in December, suggesting convective clouds become
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1 shallower in the future in the winter over the Japan Sea.
2
3 The other approach is the projection of the environmental conditions that control characteristics of severe
4 convective storms by analysing climate model results. There is high confidence that CAPE, particularly
5 summertime mean CAPE and high percentiles of the CAPE in the tropics and subtropics, increases in
6 response to global warming in an ensemble of climate models including those of CMIP5, mainly from
7 increased low-level specific humidity (Sobel and Camargo, 2011; Singh et al., 2017a; Chen et al., 2020b).
8 CIN becomes stronger over most land areas under global warming, resulting mainly from reduced low-level
9 relative humidity over land (Chen et al., 2020b). However, there are large differences within the CMIP5
10 ensemble for environmental conditions, which contribute to some degree of uncertainty (Allen, 2018).
11 Because the relation between simulated environments in models and the occurrence of severe convective
12 storms are in general insufficiently validated, the confidence level of the projection of severe convective
13 storms with the approach of the environmental conditions is generally low.
14
15 In the United States, projected changes in the environmental conditions show an increase in CAPE and no
16 changes or decreases in the vertical wind shear, suggesting favourable conditions for an increase in severe
17 convective storms in the future, but the interpretation of how tornadoes or hail will change is an open
18 question because of the strong dependence on shear (Brooks, 2013). Diffenbaugh et al. (2013) showed robust
19 increases in the occurrence of the favourable environments for severe convective storms with increased
20 CAPE and stronger low-level wind shear in response to future global warming. A downscaling approach
21 showed that the variability of the occurrence of severe convective storms increases in spring in late 21st
22 century simulations (Gensini and Mote, 2015). Future changes in hail occurrence in the United States
23 examined through convection-permitting dynamical downscaling suggested that the hail season may begin
24 earlier in the year and exhibit more interannual variability with increases in the frequency of large hail in
25 broad areas over the United States (Trapp et al., 2019). There is medium confidence that the frequency and
26 variability of the favourable environments for severe convective storms will increase in spring, and low
27 confidence for summer and autumn (Diffenbaugh et al., 2013; Gensini and Mote, 2015; Hoogewind et al.,
28 2017). The occurrence of hail events in Colorado in the United States was examined by comparing both
29 present-day and projected future climates using high-resolution model simulations capable of resolving
30 hailstorms (Mahoney et al., 2012), which showed hail is almost eliminated at the surface in the future in
31 most of the simulations, despite more intense future storms and significantly larger amounts of hail generated
32 in-cloud.
33
34 Future changes in severe convection environments show enhancement of instability with less robust changes
35 in the frequency of strong vertical wind shear in Europe (Púčik et al. 2017) and in Japan (Muramatsu et al.
36 2016). In Japan, the frequency of conditions favourable for strong tornadoes increases in spring and partly in
37 summer.
38
39 In summary, the average and maximum rain rates associated with severe convective storms increase in a
40 warming world in some regions including the USA (high confidence). There is high confidence from climate
41 models that CAPE increases in response to global warming in the tropics and subtropics, suggesting more
42 favourable environments for severe convective storms. The frequency of springtime severe convective
43 storms is projected to increase in the USA leading to a lengthening of the severe convective storm season
44 (medium confidence), evidence in other regions is limited. There is significant uncertainty about projected
45 regional changes in tornadoes, hail, and lightning due to limited analysis of simulations using convection-
46 permitting models (high confidence).
47
48
49 11.7.4 Extreme winds
50
51 Extreme winds are defined here in terms of the strongest near-surface wind speeds that are generally
52 associated with extreme storms, such as TCs, ETCs, and severe convective storms. In previous IPCC reports,
53 near-surface wind speed (including extremes), has not been assessed as a variable in its own right, but rather
54 in the context of other extreme atmospheric or oceanic phenomena. The exception was the SREX report
55 (Seneviratne et al., 2012), which specifically examined past changes and projections of mean and extreme
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1 near-surface wind speeds. A strong decline in extreme winds compared to mean winds was reported for the
2 continental northern mid-latitudes. Due to the small number of studies and uncertainties in terrestrial-based
3 surface wind measurements, the findings were assigned low confidence in the SREX. AR5 reported a
4 weakening of mean and maximum winds from the 1960s or 1970s to the early 2000s in the tropics and mid-
5 latitudes and increases in high latitudes, but with low confidence in changes in the observed surface winds
6 over land (Hartmann et al., 2013). Observed trends in mean wind speed over land and the ocean are assessed
7 in Section 2.3.1.4.4. Aspects of climate impact-drivers for winds are addressed in Section 12.3.3 and 12.5.2.3
8 and their regional changes are assessed in Section 12.4.
9
10 Observationally, although not specifically addressing extreme wind speed changes, negative surface wind
11 speed trends (stilling) were found in the tropics and mid-latitudes of both hemispheres of -0.014 m s-1 year-1,
12 while positive trends were reported at high latitudes poleward of 70 degrees, based on a review of 148
13 studies (McVicar et al., 2012a). An earlier study attributed the stilling to both changes in atmospheric
14 circulation and an increase in surface roughness due to an overall increase in vegetation cover (Vautard et
15 al., 2010). Since then, a number of additional studies have mostly confirmed these general negative mean-
16 wind trends based on anemometer data for Spain (Azorin-Molina et al., 2017), Turkey, (Dadaser-Celik and
17 Cengiz, 2014), the Netherlands, (Wever, 2012), Saudi Arabia, (Rehman, 2013), Romania, (Marin et al.,
18 2014), and China (Chen et al., 2013). Lin et al. (2013) note that wind speed variability over China is greater
19 at high elevation locations compared to those closer to mean sea level. Hande et al. (2012), using radiosonde
20 data, found an increase in surface wind speed on Macquarie Island.
21
22 A number of new studies have examined surface wind speeds over the ocean based on ship-based
23 measurements, satellite altimeters, and Special Sensor Microwave/Imagers (SSM/I) (Tokinaga and Xie,
24 2011; Zieger et al., 2014). It has been noted that wind speed trends tend to be stronger in altimeter
25 measurements, although the spatial patterns of change are qualitatively similar in both instruments (Zieger et
26 al., 2014). Liu et al. (2016) found positive trends in surface wind speeds over the Arctic Ocean in 20 years of
27 satellite observations. Small positive trends in mean wind speed were found in 33 years of satellite data,
28 together with larger trends in the 90th percentile values over global oceans (Ribal and Young, 2019). These
29 results were consistent with an earlier study that found a positive trend in 1-in-100 year wind speeds (Young
30 et al., 2012). A positive change in mean wind speeds was found for the Arabian Sea and the Bay of Bengal
31 (Shanas and Kumar, 2015) and Zheng et al. (2017) found that positive wind speed trends over the ocean
32 were larger during winter seasons than summer seasons.
33
34 Changes in extreme winds are associated with changes in the characteristics (locations, frequencies, and
35 intensities) of extreme storms, including TCs, ETCs, and severe convective storms. For TCs, as assessed in
36 Section 11.7.1.5, it is projected that the average peak TC wind speeds will increase globally with warming,
37 while the global frequency of TCs over all categories will decrease or remain unchanged; the average
38 location where TCs reach their peak wind-intensity will migrate poleward in the western North Pacific
39 Ocean as the tropics expand with warming. Frequency, intensities, and geographical distributions of extreme
40 wind events associated with TCs will change according to these TC changes. For ETCs, by the end of the
41 century, CMIP5 models show the number of ETCs associated with extreme winds will significantly decrease
42 in the mid- and high latitudes of the Northern Hemisphere in winter, with the projected decrease being larger
43 over the Atlantic (Kar-Man Chang, 2018), while it will significantly increase irrespective of the season in the
44 Southern Hemisphere (Chang, 2017)(Section 11.7.2.4). Over the ocean in the subtropics, a large ensemble of
45 60-km global model simulations indicated that extreme winds associated with storm surges will intensify
46 over 15–35°N in the Northern Hemisphere (Mori et al., 2019). On the other hand, extreme surface wind
47 speeds will mostly decrease due to decreases in the number and intensity of TCs over most tropical areas of
48 the Southern Hemisphere (Mori et al., 2019). The projected changes in the frequency of extreme winds are
49 associated with the future changes in TCs and ETCs.
50
51 Extreme cyclonic windstorms that share some characteristics with both TCs and ETCs occur regularly over
52 the Mediterranean Sea and are often referred to as “medicanes” (Ragone et al., 2018; Miglietta and Rotunno,
53 2019; Ragone et al., 2018; Miglietta and Rotunno, 2019; Zhang et al., 2020e). Medicanes pose substantial
54 threats to regional islands and coastal zones. A growing body of literature consistently found that the
55 frequency of medicanes decreases under warming, while the strongest medicanes become stronger
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1 (González-Alemán et al., 2019; Tous et al., 2016; Romero and Emanuel, 2017; Romera et al., 2017;
2 Cavicchia et al., 2014; Romero and Emanuel, 2013; Gaertner et al., 2007). This is also consistent with
3 expected global changes in TCs under warming (11.7.1). Based on the consistency of these studies, it is
4 likely that medicanes will decrease in frequency, while the strongest medicanes become stronger under
5 warming scenario projections (medium confidence).
6
7 In summary, the observed intensity of extreme winds is becoming less severe in the lower to mid-latitudes,
8 while becoming more severe in higher latitudes poleward of 60 degrees (low confidence). Projected changes
9 in the frequency and intensity of extreme winds are associated with projected changes in the frequency and
10 intensity of TCs and ETCs (medium confidence).
11
12
13 11.8 Compound events
14
15 The IPCC SREX (SREX Ch3) first defined compound events as “(1) two or more extreme events occurring
16 simultaneously or successively, (2) combinations of extreme events with underlying conditions that amplify
17 the impact of the events, or (3) combinations of events that are not themselves extremes but lead to an
18 extreme event or impact when combined”. Further definitions of compound events have emerged since the
19 SREX. Zscheischler et al. (2018) defined compound events broadly as “the combination of multiple drivers
20 and/or hazards that contributes to societal or environmental risk”. This definition is used in the present
21 assessment, because of its clear focus on the risk framework established by the IPCC, and also highlighting
22 that compound events may not necessarily result from dependent drivers. Compound events have been
23 classified into preconditioned events, where a weather-driven or climate-driven precondition aggravates the
24 impacts of a hazard; multivariate events, where multiple drivers and/or hazards lead to an impact; temporally
25 compounding events, where a succession of hazards leads to an impact; and spatially compounding events,
26 where hazards in multiple connected locations cause an aggregated impact (Zscheischler et al., 2020).
27 Drivers include processes, variables, and phenomena in the climate and weather domain that may span over
28 multiple spatial and temporal scales. Hazards (such as floods, heat waves, wildfires) are usually the
29 immediate physical precursors to negative impacts, but can occasionally have positive outcomes (Flach et
30 al., 2018).
31
32
33 11.8.1 Overview
34
35 The combination of two or more – not necessarily extreme – weather or climate events that occur i) at the
36 same time, ii) in close succession, or iii) concurrently in different regions, can lead to extreme impacts that
37 are much larger than the sum of the impacts due to the occurrence of individual extremes alone. This is
38 because multiple stressors can exceed the coping capacity of a system more quickly. The contributing events
39 can be of similar types (clustered multiple events) or of different types (Zscheischler et al., 2020). Many
40 major weather- and climate-related catastrophes are inherently of a compound nature (Zscheischler et al.,
41 2018). This has been highlighted for a broad range of hazards, such as droughts, heat waves, wildfires,
42 coastal extremes, and floods (Westra et al., 2016; AghaKouchak et al., 2020; Ridder et al., 2020). Co-
43 occurring extreme precipitation and extreme winds can result in infrastructural damage (Martius et al.,
44 2016); the compounding of storm surge and precipitation extremes can cause coastal floods (Wahl et al.,
45 2015); the combination of drought and heat can lead to tree mortality (Allen et al., 2015)(see also Section
46 11.6); wildfires increase occurrences of hailstorms and lightning (Zhang et al., 2019e). Compound storm
47 types consisting of co-located cyclone, front and thunderstorm systems have a higher chance of causing
48 extreme rainfall and extreme winds than individual storm types (Dowdy and Catto, 2017). Extremes may
49 occur at similar times at different locations (De Luca et al., 2020a,b) but affect the same system, for instance,
50 spatially-concurrent climate extremes affecting crop yields and food prices (Anderson et al., 2019; Singh et
51 al., 2018). Studies also show an increasing risk for breadbasket regions to be concurrently affected by
52 climate extremes with increasing global warming, even between 1.5°C and 2°C of global warming (Gaupp et
53 al., 2019) (Box 11.2). Concomitant extreme conditions at different locations become more probable as
54 changes in climate extremes are emerging over an increasing fraction of the land area (Sections 11.2.3,
55 11.2.4, 11.8.2, 11.8.3; Box 11.4).
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1
2 Finally, impacts may occur because of large multivariate anomalies in the climate drivers, if systems are
3 adapted to historical multivariate climate variability (Flach et al., 2017). For instance, ecosystems are
4 typically adapted to the local covariability of temperature and precipitation such that a bivariate anomaly
5 may have a large impact even though neither temperature nor precipitation may be extreme based on a
6 univariate assessment (Mahony and Cannon, 2018). Given that almost all systems are affected by weather
7 and climate phenomena at multiple space-time scales (Raymond et al., 2020), it is natural to consider
8 extremes in a compound event framework. It should be noted, however, that multi-hazard dependencies can
9 also decrease risk, for instance when hazards are negatively correlated (Hillier et al., 2020). Despite this
10 recognition, the literature on past and future changes in compound events has been limited, but is growing.
11 This section assesses examples of types of compound events in available literature.
12
13 In summary, compound events include the combination of two or more – not necessarily extreme – weather
14 or climate events that occur i) at the same time, ii) in close succession, or iii) concurrently in different
15 regions. The land area affected by concurrent extremes has increased (high confidence). Concurrent extreme
16 events at different locations, but possibly affecting similar sectors (e.g., breadbaskets) in different regions,
17 will become more frequent with increasing global warming, in particular above +2°C of global warming
18 (high confidence).
19
20
21 11.8.2 Concurrent extremes in coastal and estuarine regions
22
23 Coastal and estuarine zones are prone to a number of meteorological extreme events and also to concurrent
24 extremes. A major climati-impact driver in coastal regions around the world is floods (Chapter 12), and flood
25 occurrence may be influenced by the dependence between storm surge, extreme rainfall, river flow, but also
26 by sea level rise, waves and tides, as well as groundwater for estuaries. Floods with multiple drivers are often
27 referred to as “compound floods” (Wahl et al., 2015; Moftakhari et al., 2017; Bevacqua et al., 2020b).
28
29 At US coasts, the probability of co-occurring storm surge and heavy precipitation is higher for the
30 Atlantic/Gulf coast relative to the Pacific coast (Wahl et al., 2015). Furthermore, six studied locations on the
31 US coast with long overlapping time series show an increase in the dependence between heavy precipitation
32 and storm surge over the last century, leading to more frequent co-occurring storm surge and heavy
33 precipitation events at the present day (Wahl et al., 2015). Storm surge and extreme rainfall are also
34 dependent in most locations on the Australian coasts (Zheng et al., 2013) and in Europe along the Dutch
35 coasts (Ridder et al., 2018), along the Mediterranean Sea, the Atlantic coast and the North Sea (Bevacqua et
36 al., 2019). The probability of flood occurrence can be assessed via the dependence between storm surge and
37 river flow (Bevacqua et al., 2020a, 2020b). For instance, the occurrence of a North Sea storm surge in close
38 succession with an extreme Rhine or Meuse river discharge is much more probable due to their dependence,
39 compared to if both events would be independent (Kew et al., 2013; Klerk et al., 2015). Significant
40 dependence between high sea levels and high river discharge are found for more than half of the available
41 station observations, which are mostly located around the coasts of North America, Europe, Australia, and
42 Japan (Ward et al., 2018). Combining global river discharge with a global storm surge model, hotspots of
43 compound flooding have been discovered that are not well covered by observations, including Madagascar,
44 Northern Morocco, Vietnam, and Taiwan (Couasnon et al., 2020). In the Dutch Noorderzijlvest area, there is
45 more than a two-fold increase in the frequency of exceeding the highest warning level compared to the case
46 if storm surge and heavy precipitation were independent (van den Hurk et al., 2015). In other regions and
47 seasons, the dependence can be insignificant (Wu et al., 2018b) and there can be significant seasonal and
48 regional differences in the storm surge-heavy precipitation relationship. Assessments of flood probabilities
49 are often not based on actual flood measurements and instead are estimated from its main drivers including
50 astronomical tides, storm surge, heavy precipitation, and high streamflow. Such single driver analyses might
51 underestimate flood probabilities if multiple correlated drivers contribute to flood occurrence (e.g., van den
52 Hurk et al., 2015).
53
54 Many coastal areas are also prone to the occurrence of compound precipitation and wind extremes, which
55 can cause damage, including to infrastructure and natural environments. A high percentage of co-occurring
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1 wind and precipitation extremes are found in coastal regions and in areas with frequent tropical cyclones.
2 Finally, the combination of extreme wave height and duration is also shown to influence coastal erosion
3 processes (Corbella and Stretch, 2012).
4
5 Aspects of concurrent extremes in coastal and estuarine environments have increased in frequency and/or
6 magnitude over the last century in some regions. These include an increase in the dependence between heavy
7 precipitation and storm surge over the last century, leading to more frequent co-occurring storm surge and
8 heavy precipitation events in the present day along US coastlines (Wahl et al., 2015). In Europe, the
9 probability of compound flooding occurrence increases most strongly along the Atlantic coast and the North
10 Sea under strong warming. This increase is mostly driven by an intensification of precipitation extremes and
11 aggravated flooding probability due to sea level rise (Bevacqua et al., 2019). At the global scale and under a
12 high emissions scenario, the concurrence probability of meteorological conditions driving compound
13 flooding would increase by more than 25% on average along coastlines worldwide by 2100, compared to the
14 present (Bevacqua et al., 2020b). Sea level extremes and their physical impacts in the coastal zone arise from
15 a complex set of atmospheric, oceanic, and terrestrial processes that interact on a range of spatial and
16 temporal scales and will be modified by a changing climate, including sea level rise (McInnes et al., 2016).
17 Interactions between sea level rise and storm surges (Little et al., 2015), and sea level and fluvial flooding
18 (Moftakhari et al., 2017) are projected to lead to more frequent and more intense compound coastal flooding
19 events as sea levels continue to rise.
20
21 In summary, there is medium confidence that over the last century the probability of compound flooding has
22 increased in some locations, including along the US coastline. There is medium confidence that the
23 occurrence and magnitude of compound flooding in coastal regions will increase in the future due to both sea
24 level rise and increases in heavy precipitation.
25
26
27 11.8.3 Concurrent droughts and heat waves
28
29 Concurrent droughts and heat waves have a number of negative impacts on human society and natural
30 ecosystems. Studies since SREX and AR5 show several occurrences of observed combinations of drought
31 and heat waves in various regions.
32
33 Over most land regions, temperature and precipitation are strongly negatively correlated during summer
34 (Zscheischler and Seneviratne, 2017), mostly due to land-atmosphere feedbacks (Sections 11.1.6, 11.3.2),
35 but also because synoptic-scale weather systems favourable for extreme heat are also unfavourable for rain
36 (Berg et al., 2015). This leads to a strong correlation between droughts and heat waves (Zscheischler and
37 Seneviratne, 2017). Drought events characterized by low precipitation and extreme high temperatures have
38 occurred, for example, in California (AghaKouchak et al., 2014), inland eastern Australia (King et al., 2014),
39 and large parts of Europe (Orth et al., 2016b). The 2018 growing season was both record-breaking dry and
40 hot in Germany (Zscheischler and Fischer, 2020).
41
42 The probability of co-occurring meteorological droughts and heat waves has increased in the observational
43 period in many regions and will continue to do so under unabated warming (Herrera-Estrada and Sheffield,
44 2017; Zscheischler and Seneviratne, 2017; Hao et al., 2018; Sarhadi et al., 2018; Alizadeh et al., 2020; Wu et
45 al., 2021). Overall, projections of increases in co-occurring drought and heat waves are reported in northern
46 Eurasia (Schubert et al., 2014), Europe ; Sedlmeier et al., 2018), southeast Australia (Kirono et al., 2017),
47 multiple regions of the United States (Diffenbaugh et al., 2015; Herrera-Estrada and Sheffield 2017),
48 northwest China (Li et al., 2019c; Kong et al., 2020) and India (Sharma and Mujumdar, 2017). The dominant
49 signal is related to the increase in heat wave occurrence, which has been attributed to anthropogenic forcing
50 (11.3.4). This means that even if drought occurrence is unaffected, compound hot and dry events will be
51 more frequent (Sarhadi et al., 2018; Yu and Zhai, 2020).
52
53 Drought and heat waves are also associated with fire weather, related through high temperatures, low soil
54 moisture, and low humidity. Fire weather refers to weather conditions conducive to triggering and sustaining
55 wildfires, which generally include temperature, soil moisture, humidity, and wind (Chapter 12). Concurrent
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1 hot and dry conditions amplify conditions that promote wildfires (Schubert et al., 2014; Littell et al., 2016;
2 Hope et al., 2019, Dowdy, 2018). Burnt area extent in western US forests (Abatzoglou and Williams, 2016)
3 and particularly in California (Williams et al., 2019) has been linked to anthropogenic climate change via a
4 significant increase in vapour pressure deficit, a primary driver of wildfires. A study of the western US
5 examined the correlation between historical water-balance deficits and annual area burned, across a range of
6 vegetation types from temperate rainforest to desert (McKenzie and Littell, 2017). The relationship between
7 temperature and dryness, and wildfire, varied with ecosystem type, and the fire-climate relationship was both
8 nonstationary and vegetation-dependent. In many fire-prone regions, such as the Mediterranean and China’s
9 Daxing’anling region, projections for increased severity of future drought and heat waves may lead to an
10 increased frequency of wildfires relative to observed (Ruffault et al., 2018; Tian et al., 2017). Observations
11 show a long-term trend towards more dangerous weather conditions for bushfires in many regions of
12 Australia, which is attributable at least in part to anthropogenic climate change (Dowdy, 2018). There is
13 emerging evidence that recent regional surges in wildland fires are being driven by changing weather
14 extremes (SRCCL Ch2, Cross-Chapter Box 3; Jia et al., 2019). Between 1979 and 2013, the global burnable
15 area affected by long fire-weather seasons doubled, and the mean length of the fire-weather season increased
16 by 19% (Jolly et al., 2015). However, at the global scale, the total burned area has been decreasing between
17 1998 and 2015 due to human activities mostly related to changes in land use (Andela et al., 2017). Given the
18 projected high confidence increase in compound hot and dry conditions, there is high confidence that fire
19 weather conditions will become more frequent at higher levels of global warming in some regions. This
20 assessment is also consistent with assessments of Chapter 12 for regional projected changes in fire weather.
21 The SRCCL Ch2 assessed with high confidence that future climate variability is expected to enhance the risk
22 and severity of wildfires in many biomes such as tropical rainforests.
23
24 In summary, there is high confidence that concurrent heat waves and droughts have increased in frequency
25 over the last century at the global scale due to human influence. There is medium confidence that weather
26 conditions that promote wildfires (fire weather) have become more probable in southern Europe, northern
27 Eurasia, the US, and Australia over the last century. There is high confidence that compound hot and dry
28 conditions become more probable in nearly all land regions as global mean temperature increases. There is
29 high confidence that fire weather conditions will become more frequent at higher levels of global warming in
30 some regions.
31
32
33 [START BOX 11.4 HERE]
34
35
36 BOX 11.4: Case study: Global-scale concurrent climate anomalies at the example of the 2015-2016
37 extreme El Niño and the 2018 boreal spring/summer extremes
38
39 Occurrence of concurrent or near-concurrent extremes in different parts of a region, or in different locations
40 around the world challenges adaptation and risk management capacity. This can occur as a result of natural
41 climate variability, as climates in different parts of the world are inter-connected through teleconnections. In
42 addition, in a warming climate, the probability of having several locations being affected simultaneously by
43 e.g. hot extremes and heat waves increases strongly as a function of global warming, with detectable changes
44 even for changes as small as +0.5°C of additional global warming (Sections 11.2.5 and 11.3, Cross-chapter
45 Box 11.1). Recent articles have highlighted the risks associated with concurrent extremes over large spatial
46 scales (e.g. Lehner and Stocker, 2015; Boers et al., 2019; Gaupp et al., 2019). There is evidence that such
47 global-scale extremes associated with hot temperature extremes are increasing in occurrence (Sippel et al.,
48 2015; Vogel et al., 2019). Hereafter, the focus is on two recent global-scale events that featured concurrent
49 extremes in several regions across the world. The first focuses on concurrent extremes driven by variability
50 in tropical Pacific SSTs associated with the 2015-2016 extreme El Niño, while the second is a case study of
51 the impacts of global warming combined with abnormal atmospheric circulation patterns in the 2018 boreal
52 spring/summer.
53
54
55 [START BOX 11.4, FIGURE 1 HERE]
56
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1 Box 11.4, Figure 1: Analysis of the percentage of land area affected by temperature extremes larger than two (orange)
2 or three (blue) standard deviations in June-July-August (JJA) between 30°N and 80°N using a
3 normalization. The more appropriate estimate is the corrected normalization. These panels show
4 for both estimates a substantial increase in the overall land area affected by very high hot extremes
5 since 1990 onward. Adapted from Sippel et al. (2015)
6
7 [END BOX 11.4, FIGURE 1 HERE]
8
9
10 The extreme El Niño in 2015-2016
11
12 El Niño-Southern Oscillation (ENSO) is one of the phenomena that have the ability to bring multitudes of
13 extremes in different parts of the world, especially in the extreme cases of El Niño (Annex VI.4).
14 Additionally, the background climate warming associated with greenhouse gas forcing can significantly
15 exacerbate extremes in parts of the world even under normal El Niño conditions. The 2015-2016 El Niño
16 event was one of the three extreme El Nino events since 1980s since the availability of satellite rainfall
17 observations. According to some measures, it was the strongest El Niño over the past 145 years (Barnard et
18 al., 2017). The 2015-2016 warmth was unprecedented at the central equatorial Pacific (Niño4: 5°N–5°S,
19 150°E–150°W) and this exceptional warmth was unlikely to have occurred entirely naturally, appearing to
20 reflect an anthropogenically forced trend (Newman et al., 2018)). In particular, its signal was seen in very
21 high monthly Global Mean Surface Temperature (GMST) values in late 2015 and early 2016, contributing to
22 the highest record of GMST in 2016 (Section 2.3.1.1). Both the ENSO amplitude and the frequency of high-
23 magnitude events since 1950 is higher than over the pre-industrial period (medium confidence; Section
24 2.4.2), suggesting that global extremes similar to those associated with the 2015-2016 El Niño would occur
25 more frequently under further increases in global warming. Hereafter, the 2015-2016 El Niño event is
26 referred to as “the 2015-2016 extreme El Niño” (Annex VI.4.1). A brief summary of extreme events that
27 happened in 2015-2016 is provided in Section 6.2.2, 6.5.1.1 of the Special Report on the Ocean and
28 Cryosphere in a Changing Climate (SROCC’s). We provide some highlights illustrating extremes that
29 occurred in different parts of the world during the 2015-2016 extreme El Niño in BOX11.4-Table 1, as well
30 as a short summary hereafter.
31
32 Several regions were strongly affected by droughts in 2015, including Indonesia, Australia, the Amazon
33 region, Ethiopia, Southern Africa, and Europe. As a result, global measurements of land water anomalies
34 were particularly low in that year (Humphrey et al., 2018). In 2015, Indonesia experienced a severe drought
35 and forest fire causing pronounced impact on economy, ecology and human health due to haze crisis (Field
36 et al., 2016; Huijnen et al., 2016; Patra et al., 2017; Hartmann et al., 2018). The northern part of Australia
37 experienced high temperatures and low precipitation between late 2015 and early 2016, and the extensive
38 mangrove trees were damaged along the Gulf of Carpentaria in northern Australia (Duke et al., 2017). The
39 Amazon region experienced the most intense droughts of this century in 2015-2016. This drought was more
40 severe than the previous major droughts that occurred in the Amazon in 2005 and 2010 (Lewis et al., 2011;
41 Erfanian et al., 2017; Panisset et al., 2018). The 2015-2016 Amazon drought impacted the entirety of South
42 America north of 20°S during the austral spring and summer (Erfanian et al., 2017). It also increased forest
43 fire incidence by 36% compared to the preceding 12 years (Aragão et al., 2018) and as a consequence,
44 increased the biomass burning outbreaks and the carbon monoxide (CO) concentration in the area, affecting
45 air quality (Ribeiro et al., 2018). This out-of-season drought affected the water availability for human
46 consumption and agricultural irrigation and it also left rivers with very low water levels, without conditions
47 of ship transportation, due to large sandbanks, preventing the arrival of food, medicines, and fuels (INMET,
48 2017). Eastern African countries were impacted by drought in 2015. It was found that the drought in
49 Ethiopia, which was the worst in several decades, was associated with the 2015-2016 extreme El Niño that
50 developed early in the year (Blunden and Arndt, 2016; Philip et al., 2018b). It was suggested that
51 anthropogenic warming contributed to the 2015 Ethiopian and southern African droughts by increasing SSTs
52 and local air temperatures (Funk et al., 2016, 2018b; Yuan et al., 2018a). It has also been suggested that the
53 2015-2016 extreme El Niño affected circulation patterns in Europe during the 2015-2016 winter (Geng et al.,
54 2017; Scaife et al., 2017).
55
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1 It was identified that 2015 was a year of a particularly high CO2 growth rate, possibly related to some of the
2 mentioned droughts, in particular in Indonesia and the Amazon region, leading to higher CO2 release in
3 combination with less CO2 uptake from land areas (Humphrey et al., 2018). The impact of the 2015-2016
4 extreme El Niño on vegetation systems via drought was also shown from satellite data (Kogan and Guo,
5 2017). Overall, tropical forests were a carbon source to the atmosphere during the 2015–2016 El Niño–
6 related drought, with some estimates suggesting that up to 2.3 PgC were released (Brando et al., 2019).
7
8 The 2015-2016 extreme El Niño has induced extreme precipitation in some regions. Severe rainfall events
9 were observed in Chennai city in India in Devember 2015 and Yangtze river region in China in June-July
10 2016, and it was shown that these rainfall events are partly attributed to the 2015-2016 extreme El Niño (van
11 Oldenborgh et al., 2016; Boyaj et al., 2018; Sun and Miao, 2018; Yuan et al., 2018b; Zhou et al., 2018).
12
13 In 2015, the activity of tropical cyclones was notably high in the North Pacific (Blunden and Arndt, 2016).
14 Over the western North Pacific, the number of category 4 and 5 Tropical Cyclones (TCs) was 13, which is
15 more than twice its typical annual value of 6.3 (Zhang et al., 2016a). Similarly, a record-breaking number of
16 TCs was observed in the eastern North Pacific, particularly in the western part of that domain (Collins et al.,
17 2016; Murakami et al., 2017a). These extraordinary TC activities were related to the average SST anomaly
18 during that year, which were associated with the 2015-2016 extreme El Niño and the positive phase of the
19 Pacific Meridional Mode (PMM) (Murakami et al., 2017a; Hong et al., 2018; Yamada et al., 2019).
20 However, it has been suggested that the intense TC activities in both the western and the eastern North
21 Pacific in 2015 were not only due to the El Niño, but also to a contribution of anthropogenic forcing
22 (Murakami et al., 2017a; Yang et al., 2018d). The impact of the Indian Ocean SST also was suggested to
23 contribute to the extreme TC activity in the western North Pacific in 2015 (Zhan et al., 2018). In contrast, in
24 Australia, it was the least active TC season since satellite records began in 1969-70 (Blunden and Arndt,
25 2017).
26
27
28 [START BOX 11.4, TABLE 1 HERE]
29
30 Box 11.4, Table 1: List of events related to the 2015-2016 Extreme El Niño in the literature.
31
Region Period Events References
Indonesia July 2015 to June droughts, forest fire (Field et al., 2016; Huijnen et al., 2016; Patra et
2016 al., 2017; Hartmann et al., 2018)
Northern Australia Between late 2015 high temperature and drought (Duke et al., 2017)
and early 2016
Amazon September 2015 to droughts, forest fire (Jiménez-Muñoz et al., 2016; Erfanian et al.,
May 2016 2017; Aragão et al., 2018; Panisset et al., 2018;
Ribeiro et al., 2018)
The entirety of Austral spring and droughts (Erfanian et al., 2017)
South America 2015-2016 summer
north of 20°S
Ethiopia February-September droughts (Blunden and Arndt, 2016; Philip et al., 2018b)
2015
Southern Africa November 2015– droughts (Funk et al., 2016, 2018a; Blamey et al., 2018;
April 2016 Yuan et al., 2018a)
Europe Boreal 2015-2016 effects on of circulation (Geng et al., 2017; Scaife et al., 2017)
winter patterns
India May 2016 high temperature (van Oldenborgh et al., 2018)
India December 2015 extreme rainfall (van Oldenborgh et al., 2016; Boyaj et al.,
2018)
China June-July 2016 extreme rainfall (Sun and Miao, 2018; Yuan et al., 2018b; Zhou
et al., 2018)
Western North Boreal summer 2015 the large number (13) of (Blunden and Arndt, 2016; Mueller et al.,
Pacific category 4 and 5 tropical 2016a; Zhang et al., 2016b; Hong et al., 2018;
cyclones Yamada et al., 2019)
Eastern North Boreal summer 2015 a record-breaking number of (Collins et al., 2016; Murakami et al., 2017a)
Pacific tropical cyclones
Global 2015-2016 El Niño high CO2 release to the (Humphrey et al., 2018; Brando et al., 2019)
atmosphere associated with
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droughts and fires in several
affected regions
1
2
3 [END BOX 11.4, TABLE 1 HERE]
4
5
6 Global-scale temperature extremes and concurrent precipitation extremes in boreal 2018 spring and
7 summer
8
9 In the 2018 boreal spring-summer season (May-August), wide areas of the mid-latitudes in the Northern
10 Hemisphere experienced heat extremes and in part enhanced drought (Kornhuber et al., 2019; Vogel et al.,
11 2019; Box 11.3, Figure 2). The reported impacts included the following (Vogel et al., 2019): 90 deaths from
12 heat strokes in Quebec (Canada), 1469 deaths from heat strokes in Japan (Shimpo et al., 2019a), 48 heat-
13 related deaths in South Korea (Min et al., 2020), heat warning affecting 90,000 students in the USA, fires in
14 numerous countries (Canada (British Columbia), USA (California), Lapland, Latvia), crop losses in the UK,
15 Germany and Switzerland (Vogel et al., 2019) and overall in central and northern Europe (leading to yield
16 reductions of up to 50% for the main crops; Toreti et al., 2019), fish deaths in Switzerland, and melting of
17 roads in the Netherlands and the UK, among others. In addition to the numerous hot and dry extremes, an
18 extremely heavy rainfall event occurred over wide areas of Japan from 28 June to 8 July 2018 (Tsuguti et al.,
19 2018), which was followed by a heat wave (Shimpo et al., 2019b). The heavy precipitation event caused
20 more than 230 deaths in Japan, and was named as “the Heavy Rain Event of July 2018”.
21
22 The heavy precipitation event was characterized by unusually widespread and persistent rainfall and locally
23 anomalous total precipitation led by band-shaped precipitation systems, which are frequently associated with
24 heavy precipitation events in East Asia (Kato, 2020; Section 11.7.3). The extreme rainfall in Japan was
25 caused by anomalous moisture transport with a combination of abnormal jet condition (Takemi and Unuma,
26 2019; Takemura et al., 2019; Tsuji et al., 2019; Yokoyama et al., 2020), which can be viewed as an
27 atmospheric river (Yatagai et al., 2019; Sections 8.2.2.8, 11.7.2) caused by intensified inflow velocity and
28 high SST around Japan (Kawase et al., 2019; Sekizawa et al., 2019).
29
30 This precipitation event and the subsequent heat wave are related to abnormal condition of the jet and North
31 Pacific Subtropical High in this month (Shimpo et al., 2019a; Ren et al., 2020), which caused extreme
32 conditions from Europe, Eurasia, and North America (Kornhuber et al., 2019; Box 11.4, Figure 2). A role of
33 Atlantic SST anomaly on the meandering jets and the subtropical high have been suggested (Liu et al.,
34 2019a). These dynamic and thermodynamic components generally have substantial influence on extreme
35 rainfall in East Asia (Oh et al., 2018), but it is under investigation whether these factors were due to
36 anthropogenic forcing.
37
38
39 [START BOX 11.4, FIGURE 2 HERE]
40 Box 11.4, Figure 2: Meteorological conditions in July 2018. The color shading shows the monthly mean near-
41 surface air temperature anomaly with respect to 1981 to 2010. Contour lines indicate the
42 geopotential height in m, highlighted are the isolines on 12'000 m and 12'300 m, which indicate
43 the approximate positions of the polar-front jet and subtropical jet, respectively. The light blue-
44 green ellipse shows the approximate extent of the strong precipitation event that occurred at the
45 beginning of July in the region of Japan and Korea. All data is from the global ECMWF
46 Reanalysis v5 (ERA5, Hersbach et al., 2020).
47 [END BOX 11.4, FIGURE 2 HERE]
48
49
50 Regarding the hot extremes that occurred across the Northern Hemisphere in the 2018 boreal May-July time
51 period, Vogel et al. (2019) found that the event was unprecedented in terms of the total area affected by hot
52 extremes (on average about 22% of populated and agricultural areas in the Northern Hemisphere) for that
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1 period, but was consistent with a +1°C climate which was the estimated global mean temperature anomaly
2 around that time (for 2017; SR1.5). This study also found that events similar to the 2018 May-July
3 temperature extremes would approximately occur 2 out of 3 years under +1.5°C of global warming, and
4 every year under +2°C of global warming. Imada et al. (2019) also suggests that the mean annual occurrence
5 of extremely hot days in Japan will be expected to increase by 1.8 times under a global warming level of 2°C
6 above pre-industrial levels. Kawase et al. (2019) showed that the extreme rainfall in Japan during this event
7 was increased by approximately 7% due to recent rapid warming around Japan. Hence, it is virtually certain
8 that these 2018 concurrent events would not have occurred without human-induced global warming.
9 Concurrent events of this type are also projected to happen more frequently under higher levels of global
10 warming. On the other hand, there is currently low confidence in projected changes in the frequency or
11 strength of the anomalous circulation patterns leading to concurrent extremes (e.g. Cross-Chapter Box 10.1).
12
13 The case studies presented in this Box illustrate the current state of knowledge regarding the contribution of
14 human-induced climate change to recent concurrent extremes in the global domain. Recent years have seen a
15 more frequent occurrence of such events. The heat wave in Europe in the 2019 boreal summer and its
16 coverage in the global domain is an additional example (Vautard et al., 2020a). However, there are still very
17 few studies investigating which types of concurrent extreme events could occur under increasing global
18 warming. It has been noted that such events could also be of particular risk for concurrent impacts in the
19 world’s breadbaskets (Zampieri et al., 2017; Kornhuber et al., 2020).
20
21 In summary, the 2015-2016 extreme El Niño and the 2018 boreal spring/summer extremes were two
22 examples of recent concurrent extremes. The El Niño event in 2015-2016 was one of the three extreme El
23 Niño events since 1980s and there are many extreme events concurrently observed in this period including
24 droughts, heavy precipitation, and more frequent intense tropical cyclones. Both the ENSO amplitude and
25 the frequency of high-magnitude events since 1950 is higher than over the pre-industrial period (medium
26 confidence), suggesting that global extremes similar to those associated with the 2015-2016 El Niño would
27 occur more frequently under further increases in global warming. The 2018 boreal spring/summer extremes
28 were characterized by heat extremes and enhanced droughts in wide areas of the mid-latitudes in the
29 Northern Hemisphere and extremely heavy rainfall in East Asia. These concurrent events were generally
30 related to abnormal condition of the jet and North Pacific Subtropical High, but also amplified by
31 background global warming. It is virtually certain that these 2018 concurrent extreme events would not have
32 occurred without human-induced global warming. Recent years have seen a more frequent occurrence of
33 such concurrent events. However, it is still unknown which types of concurrent extreme events could occur
34 under increasing global warming.
35
36
37 [END BOX 11.4 HERE]
38
39
40 11.9 Regional information on extremes
41
42 This section complements the assessments of changes in temperature extremes (Section 11.3), heavy
43 precipitation (Section 11.4), and droughts (Section 11.6), by providing additional regional details. Owing to
44 the large number of regions and space limitations, the regional assessment for each of the AR6 reference
45 regions (see Section 1.5.2.2 for a description) is presented here in a set of tables. The tables are organized
46 according to types of extremes (temperature, heavy precipitation, droughts) for Africa (Tables 11.4-11.6),
47 Asia (Table 11.7-11.9), Australasia (Tables 11.10-11.12), Central and South America (Tables 11.13-11.15),
48 Europe (Tables 11.16-11.18), and North America (Tables 11.19-11.21). Each table contains regional
49 assessments for observed changes, the human contribution to the observed changes, and projections of
50 changes in these extremes at 1.5°C, 2°C and 4°C of global warming. Expanded versions of the tables with
51 full evidence and rationale for assessments are provided in the Chapter Appendix (Tables 11.A.4-11.A.21).
52
53
54 11.9.1 Overview
55
56 Sections 11.9.2, 11.9.3., and 11.9.4 provide brief summaries of the underlying evidence used to derive the
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Final Government Distribution Chapter11 IPCC AR6 WGI
1 regional assessments for temperature extremes, heavy precipitation events, and droughts, respectively. The
2 assessments take into account evidence from studies based on global datasets (global studies), as well as
3 regional studies. Global studies include analyses for all continents and AR6 regions with sufficient data
4 coverage, and provide an important basis for cross-region consistency, as the same data and methods are
5 used for all regions. However, individual regional studies may include additional information that is missed
6 in global studies and thus provide an important regional calibration for the assessment.
7
8 The assessments are presented using the calibrated confidence and likelihood language (Box 1.1). Low
9 confidence is assessed when there is limited evidence, either because of a lack of available data in the region
10 and/or a lack of relevant studies. Low confidence is also assessed when there is a lack of agreement on the
11 evidence of a change, which may be due to large variability or inconsistent changes depending on the
12 considered subregions, time frame, models, assessed metrics, or studies. In cases when the evidence is
13 strongly contradictory, for example with substantial regional changes of opposite sign, “mixed signal” is
14 indicated. With an assessment of low confidence, the direction of change is not indicated in the tables. A
15 direction of change (increase or decrease) is provided with an assessment of medium confidence, high
16 confidence, likely, or higher likelihood levels. Likelihood assessments are only provided in the case of high
17 confidence. In some cases, there may be confidence in a small or no change.
18
19 For projections, changes are assessed at three global warming levels (GWLs, CC-Box 11.1): 1.5°C, 2°C and
20 4°C. Literature based both on GWL projections and on scenario-based projections is used for the
21 assessments. In the case of literature on scenario-based projections, a mapping between scenarios/time
22 frames and GWLs was performed as documented in CC-Box 11.1. Projections of changes in temperature and
23 precipitation extremes are assessed relative to two different baselines: the recent past (1995-2014) and pre-
24 industrial (1850-1900). With smaller changes relative to the variability, in particular because droughts
25 happen on longer timescales compared to extremes of daily temperature and precipitation, it is more difficult
26 to distinguish changes in drought relative to the recent past. As such, changes in droughts are assessed
27 relative to the pre-industrial baseline, unless indicated otherwise.
28
29
30 11.9.2 Temperature extremes
31
32 Tables 11.4, 11.7, 11.10, 11.13, 11.16, and 11.19 include assessments for past chn temperature extremes and
33 their attribution, as well as future projections. The evidence is mostly drawn from changes in metrics based
34 on daily maximum and minimum temperatures, similar to those used in Section 11.3. The regional
35 assessments start from global studies that used consistent analyses for all regions globally with sufficient
36 data. This includes Dunn et al. (2020) for observed changes and Li et al. (2020) and the Chapter 11
37 Supplementary Material (11.SM) for projections with the CMIP6 multi-model ensemble. Evidence from
38 regional studies, and those based on the CMIP5 multi-model ensemble or CORDEX simulations, are then
39 used to refine the confidence assessments. For attribution, Seong et al. (2020) provide a consistent analysis
40 for AR6 regions and Wang et al. (2017) for SREX regions. Additional regional studies, including event
41 attribution analyses (Section 11.2), are used when available. In some regions that were not analysed in Seong
42 et al. (2020) and with no known event attribution studies, medium confidence of a human contribution is
43 assessed when there is strong evidence of changes from observations that are in the direction of model
44 projected changes for the future, the magnitude of projected changes increases with global warming, and
45 there is no other evidence to the contrary. Understanding of how temperature extremes change with the mean
46 temperature and overwhelming evidence of a human contribution to the observed larger-scale changes in the
47 mean temperature and temperature extremes further support this assessment.
48
49
50 11.9.3 Heavy precipitation
51
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1 Tables 11.5, 11.8, 11.11, 11.14, 11.17, and 11.20 include assessments for past changes in heavy precipitation
2 events and their attribution, as well as future projections. The evidence is mostly drawn from changes in
3 metrics based on one-day or five-day precipitation amounts, as addressed in Section 11.4. Similar to
4 temperature extremes, the assessment of changes in heavy precipitation uses global studies, including Dunn
5 et al. (2020) and Sun et al. (2020) for observed changes, and Li et al. (2020) and the Chapter 11
6 Supplementary Material (11.SM) for projected changes using the CMIP6 multi-model ensemble. For
7 attribution, Paik et al. (2020) provided continental analyses where data coverage was sufficient, but no
8 attribution studies based on global data are available for the regional scale. For each region, regional studies,
9 and studies based on the CMIP5 multi-model ensemble or CORDEX simulations, are also considered in the
10 assessments for past changes, attribution, and projections.
11
12
13 11.9.4 Droughts
14
15 Tables 11.6, 11.9, 11.12, 11.15, 11.18, and 11.21 provide regional tables on past, attributed and projected
16 changes in droughts. The assessment is subdivided in three drought categories corresponding to four drought
17 types: i) meteorological droughts, ii) agricultural and ecological droughts, and iii) hydrological droughts (see
18 Section 11.6). A list of metrics and global studies used for the assessments is provided below. The evidence
19 from global studies is complemented in each continent with evidence from regional studies. An overview of
20 studies considered for the assessments in projections is provided in Table 11.3.
21
22 Meteorological droughts are assessed based on observed and projected changes in precipitation-only metrics
23 such as the Standardized Precipitation Index (SPI) and Consecutive Dry Days (CDD). Observed changes are
24 assessed based on two global studies, Dunn et al. (2020) for CDD and Spinoni et al. (2019) for SPI. For
25 projections, evidence for changes at 1.5°C and 2°C of global warming is drawn from Xu et al. (2019) and
26 Touma et al. (2015) (based on RCP8.5 for 2010-2054 compared to 1961-2005) for SPI (CMIP5) and the
27 Chapter 11 Supplementary Material (11.SM) for CDD (CMIP6). For projections at 4°C of global warming,
28 evidence is drawn from several sources, including Touma et al. (2015) and Spinoni et al. (2020) for SPI
29 (from CMIP5 and CORDEX, respectively), and the Chapter 11 Supplementary Material (11.SM) for CDD
30 (CMIP6). No global-scale studies are available for the attribution of meteorological drought, and thus this
31 assessment is based on regional detection and attribution or event attribution studies.
32
33 Agricultural and ecological droughts are assessed based on observed and projected changes in total column
34 soil moisture, complemented by evidence on changes in surface soil moisture, water-balance (precipitation
35 minus evapotranspiration (ET)) and metrics driven by precipitation and atmospheric evaporative demand
36 (AED) such as the SPEI and PDSI (Section 11.6). In the case of the latter, only studies including estimates
37 based on the Penman-Monteith equation (SPEI-PM and PDSI-PM) are considered because of biases
38 associated with temperature-only approaches (Section 11.6). In arid regions in which AED-based metrics can
39 increase strongly in projections, more weight is given to soil moisture projections. For observed changes,
40 evidence is drawn from several sources: Padrón et al. (2020) for changes in precipitation minus ET, as well
41 as soil moisture from the multi-model Land Surface Snow and Soil Moisture Model Intercomparison Project
42 within CMIP6 (LS3MIP, Van Den Hurk et al., 2016; Chapter 11 Supplementary Material (11.SM)); Greve et
43 al. (2014) for changes in precipitation minus ET, and precipitation minus AED; Spinoni et al. (2019) for
44 changes in SPEI-PM; and Dai and Zhao (2017) for changes in PDSI-PM. For projections at 1.5°C of global
45 warming, evidence is drawn from Xu et al. (2019) based on CMIP5 and the Chapter 11 Supplementary
46 Material (11.SM) based on CMIP6 for changes in total column and surface soil moisture, and from Naumann
47 et al. (2018) for changes in SPEI-PM, based on EC-Earth simulations driven with SSTs from seven CMIP5
48 ESMs. For projections at 2°C of global warming, evidence is drawn from Xu et al. (2019) based on CMIP5,
49 and Cook et al. (2020) (SSP1-2.6, 2071-2100 compared to pre-industrial) and the Chapter 11 Supplementary
50 Material (11.SM) based on CMIP6, for changes in total column and surface soil moisture; evidence is also
51 drawn from Naumann et al. (2018) for changes in SPEI-PM. For projections at 4°C of global warming,
52 evidence is mostly drawn from Cook et al. (2020) (SSP3-7.0, 2071-2100) and the Chapter 11 Supplementary
53 Material (11.SM) based on CMIP6 for changes in total column and surface soil moisture, and from Vicente-
54 Serrano et al. (2020) for changes in SPEI-PM based on CMIP5. No global-scale studies with regional-scale
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1 information are available for the attribution of agricultural and ecological droughts, and thus this assessment
2 is based on regional detection and attribution or event attribution studies.
3
4 Hydrological droughts are assessed based on observed and projected changes in low flows, complemented
5 by information on changes in mean runoff. For observed changes, evidence is drawn from three studies (Dai
6 and Zhao, 2017; Gudmundsson et al., 2019, 2021). For projected changes at 1.5°C of global warming,
7 evidence is drawn from Touma et al. (2015) based on analyses of the Standardized Runoff Index (SRI)
8 (CMIP5, based on 2010-2054 compared to 1961-2005), complemented with regional studies when available.
9 For projected changes at 2°C of global warming, evidence is also drawn from Cook et al. (2020) for changes
10 in runoff in CMIP6 (Scenario SSP1-2.6, 2071-2100), and from Zhai et al. (2020) for changes in low flows
11 based on simulations with a single model. For projected changes at 4°C of global warming, evidence is
12 drawn from Touma et al. (2015) based on CMIP5 analyses of SRI, Cook et al. (2020) for changes in surface
13 and total runoff based on CMIP6, and Giuntoli et al. (2015) for changes in low flows based on the Inter-
14 Sectoral Impact Model Intercomparison Project (ISI-MIP) based on six Global Hydrological Models
15 (GHMs) and five GCMs, including an analysis of inter-model signal-to-noise ratio. One global-scale study
16 with regional-scale information is available for the attribution of hydrological droughts (Gudmundsson et al.,
17 2021), but only in a few AR6 regions. This information was complemented with evidence from regional
18 detection and attribution, and event attribution studies when available.
19
20
21 [START TABLE 11.3 HERE]
22
23 Table 11.3: Global analyses considered for the assessments of drought projections. “MET” refers to meteorological
24 droughts, “AGR/ECOL” to agricultural and ecological droughts, and “HYDR” to hydrological droughts
25
Reference Model data Index Drought type Projection horizon(s) Baseline
Chapter 11 Suppl. CMIP6 CDD, Soil moisture MET 1.5°C, 2°C, 4°C 1850-1900
Material (11.SM) (total, surface)
Cook et al. (2020) CMIP6 Soil moisture (total, AGR/ECOL, 2071-2011, SSP1-2.6 (~2°C, CC-Box 1850-1900
surface), Runoff (total, HYDR 11.1; Chapter 4, Table 4.2)
surface) 2071-2011, SSP3-7-3 (~4°C, CC-Box
11.1; Chapter 4, Table 4.2)
Xu et al. (2019) CMIP5 SPI, Soil moisture MET, 1.5°C, 2°C 1971-2000
(total, surface) AGR/ECOL
Touma et al. (2015) CMIP5 SPI, SRI MET, HYDR 2010-2054, RCP8.5 (~1.5°C; CC-Box 1961-2005
11.1, 11.SM.1)
2055-2099, RCP8.5 (~3.5°C, CC-Box
11.1, 11.SM.1)
Spinoni et al. (2020) CORDEX SPI MET 2071-2100, RCP4.5 (~2.5°C, CC-Box 1981-2010
(CMIP5 driving 11.1, 11.SM.1)
GCMs, RCMs) 2071-2100, RCP8.5 (~4.5°C, CC-Box
11.1, 11.SM.1)
Naumann et al. 1 GCM (EC- SPEI-PM AGR/ECOL 1.5°C, 2°C, (3°C) 0.6°C
(2018) EARTH3-HR
v3.1) driven with
SST fields from 7
CMIP5 GCMs
Vicente-Serrano et CMIP5 SPEI-PM AGR/ECOL 2070-2100, RCP8.5 (~4.5°C, CC-Box 1970-2000
al. (2020) 11.1, 11.SM.1)
Giuntoli et al. ISI-MIP (6 GHMs Low-flows days HYDR 2066-2099, RCP8-5 (~4°C, CC-Box 1972-2005
(2015) and 5 CMIP5 11.1, 11.SM.1)
GCMs)
Zhai et al. (2020) 1 GHM (VIC) Extreme low runoff HYDR 1.5°C, 2°C 2006-2015
driven by 4
CMIP5 GCMs
26
27
28 [END TABLE 11.3 HERE]
29
30
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1 Frequently Asked Questions
2
3 FAQ 11.1: How do changes in climate extremes compare with changes in climate averages?
4 Human-caused climate change alters the frequency and intensity of climate variables (e.g., surface
5 temperature) and phenomena (e.g., tropical cyclones) in a variety of ways. We now know that the ways in
6 which average and extreme conditions have changed (and will continue to change) depend on the variable
7 and the phenomenon being considered. Changes in local surface temperature extremes follow closely the
8 corresponding changes in local average surface temperatures. On the contrary, changes in precipitation
9 extremes (heavy precipitation) generally do not follow those in average precipitation and can even move in
10 the opposite direction (e.g., with average precipitation decreasing but extreme precipitation increasing).
11 Climate change will manifest very differently depending on which region, which season and which variable
12 we are interested in. For example, over some parts of the Arctic, temperatures will warm at rates about 3-4
13 times higher during winter compared to summer months. And in summer, most of northern Europe will
14 experience larger temperatures increases than most places in Southeast South America and Australasia, with
15 differences that can be larger than 1°C depending on the level of global warming. In general, differences
16 across regions and seasons arise because the underlying physical processes differ drastically across regions
17 and seasons.
18 Climate change will also manifest differently for different weather regimes and can lead to contrasting
19 changes in average and extreme conditions. Observations of the recent past and climate model projections
20 show that, in most places, changes in daily temperatures are dominated by a general warming in which both
21 the climatological average and extreme values are shifted towards higher temperatures, making warm
22 extremes more frequent and cold extremes less frequent. The top panels in FAQ 11.1, Figure 1 show
23 projected changes in surface temperature for long-term average conditions (left) and for extreme hot days
24 (right) during the warm season (summer in mid- to high-latitudes). Projected increases in long-term average
25 temperature differ substantially in different places, varying from less than 3°C in some places in central
26 South Asia and southern South America to over 7°C in some places in North America, north Africa and the
27 Middle East. Changes in extreme hot days follow changes in average conditions quite closely, although in
28 some places the warming rates for extremes can be intensified (e.g., southern Europe and the Amazon basin)
29 or weakened (e.g., northern Asia and Greenland) compared to average values.
30 Recent observations and global and regional climate model projections point to changes in precipitation
31 extremes (including both rainfall and snowfall extremes) differing drastically from those in average
32 precipitation. The bottom panels in FAQ 11.1, Figure 1 show projected changes in the long-term average
33 precipitation (left) and in heavy precipitation (right). Averaged precipitation changes show striking regional
34 differences, with substantial drying in places such as southern Europe and northern South America and
35 wetting in places such as Middle East and southern South America. Changes in extreme heavy precipitation
36 are much more uniform, with systematic increases over nearly all land regions. The physical reasons behind
37 the different response of averaged and extreme precipitation are now well understood. The intensification of
38 extreme precipitation is driven by the increase in atmospheric water vapour (about 7% per 1°C of warming
39 near the surface), although this is modulated by various dynamical changes. In contrast, changes in average
40 precipitation are driven not only by moisture increases but also by slower processes that constrain future
41 changes to on be only about 2–3% per 1°C of warming near the surface.
42 In summary, the specific relationship between changes in average and extreme conditions strongly depends
43 on the variable or phenomenon being considered. At the local scale, average and extreme surface
44 temperature changes are strongly related, while average and extreme precipitation changes are often weakly
45 related. For both variables, the changes in average and extreme conditions vary strongly across different
46 places due to the effect of local and regional processes.
47
48 [START FAQ 11.1, FIGURE 1 HERE]
FAQ 11.1, Figure 1: Global maps of future changes in surface temperature (top panels) and precipitation (bottom
panels) for long-term average (left) and extreme conditions (right). All changes were estimated
using the CMIP6 ensemble mean for a scenario with a global warming of 4°C relative to 1850-
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1900 temperatures. Average surface temperatures refer to the warmest three-month season
(summer in mid- to high-latitudes) and extreme temperature refer to the hottest day in a year.
Precipitation changes, which can include both rainfall and snowfall changes, are normalized by
1850-1900 values and shown in percentage; extreme precipitation refers to the largest daily
rainfall in a year.
1 [END FAQ 11.1, FIGURE 1 HERE]
2
3 [END FAQ 11.1 HERE]
4
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1 [START FAQ 11.2 HERE]
2
3 FAQ 11.2: Will unprecedented extremes occur as a result of human-induced climate change?
4
5 Climate change has already increased the magnitude and frequency of extreme hot events and decreased the
6 magnitude and frequency of extreme cold events, and, in some regions, intensified extreme precipitation
7 events. As the climate moves away from its past and current states, we will experience extreme events that
8 are unprecedented, either in magnitude, frequency, timing or location. The frequency of these unprecedented
9 extreme events will increase with increasing global warming. Additionally, the combined occurrence of
10 multiple unprecedented extremes may result in large and unprecedented impacts.
11
12 Human-induced climate change has already affected many aspects of the climate system. In addition to the
13 increase in global surface temperature, many types of weather and climate extremes have changed. In most
14 regions, the frequency and intensity of hot extremes have increased and those of cold extremes have
15 decreased. The frequency and intensity of heavy precipitation events have increased at a global scale and
16 over a majority of land regions. Although extreme events such as land and marine heatwaves, heavy
17 precipitation, drought, tropical cyclones, and associated wildfires and coastal flooding have occurred in the
18 past and will continue to occur in the future, they often come with different magnitudes or frequencies in a
19 warmer world. For example, future heatwaves will last longer and have higher temperatures, and future
20 extreme precipitation events will be more intense in several regions. Certain extremes, such as extreme cold,
21 will be less intense and less frequent with increasing warming.
22
23 Unprecedented extremes – that is, events not experienced in the past – will occur in the future in five
24 different ways (FAQ 11.2, Figure 1). First, events that are considered to be extreme in the current climate
25 will occur in the future with unprecedented magnitudes. Second, future extreme events will also occur with
26 unprecedented frequency. Third, certain types of extremes may occur in regions that have not previously
27 encountered those types of events. For example, as the sea level rises, coastal flooding may occur in new
28 locations, and wildfires are already occurring in areas, such as parts of the Arctic, where the probability of
29 such events was previously low. Fourth, extreme events may also be unprecedented in their timing. For
30 example, extremely hot temperatures may occur either earlier or later in the year than they have in the past.
31
32 Finally, compound events, where multiple extreme events of either different or similar types occur
33 simultaneously and/or in succession, may be more probable or severe in the future. These compound events
34 can often impact ecosystems and societies more strongly than when such events occur in isolation. For
35 example, a drought along with extreme heat will increase the risk of wildfires and agriculture damages or
36 losses. As individual extreme events become more severe as a result of climate change, the combined
37 occurrence of these events will create unprecedented compound events. This could exacerbate the intensity
38 and associated impacts of these extreme events.
39
40 Unprecedented extremes have already occurred in recent years, relative to the 20th century climate. Some
41 recent extreme hot events would have had very little chance of occurring without human influence on the
42 climate (see FAQ 11.3). In the future, unprecedented extremes will occur as the climate continues to warm.
43 Those extremes will happen with larger magnitudes and at higher frequencies than previously experienced.
44 Extreme events may also appear in new locations, at new times of the year, or as unprecedented compound
45 events. Moreover, unprecedented events will become more frequent with higher levels of warming, for
46 example at 3°C of global warming compared to 2°C of global warming.
47
48 [START FAQ 11.2, FIGURE 1 HERE]
49
50 FAQ 11.2, Figure 1: New types of unprecedented extremes that will occur as a result of climate change.
51
52 [END FAQ 11.2, FIGURE 1 HERE]
53
54 [END FAQ 11.2 HERE]
55
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1 [START FAQ 11.3 HERE]
2
3 FAQ 11.3: Did climate change cause that recent extreme event in my country?
4
5 While it is difficult to identify the exact causes of a particular extreme event, the relatively new science of
6 event attribution is able to quantify the role of climate change in altering the probability and magnitude of
7 some types of weather and climate extremes. There is strong evidence that characteristics of many individual
8 extreme events have already changed because of human-driven changes to the climate system. Some types of
9 highly impactful extreme weather events have occurred more often and have become more severe due to
10 these human influences. As the climate continues to warm, the observed changes in the probability and/or
11 magnitude of some extreme weather events will continue as the human influences on these events increase.
12
13 It is common to question whether human-caused climate change caused a major weather- and climate-related
14 disaster. When extreme weather and climate events do occur, both exposure and vulnerability play an
15 important role in determining the magnitude and impacts of the resulting disaster. As such, it is difficult to
16 attribute a specific disaster directly to climate change. However, the relatively new science of event
17 attribution enables scientists to attribute aspects of specific extreme weather and climate events to certain
18 causes. Scientists cannot answer directly whether a particular event was caused by climate change, as
19 extremes do occur naturally and any specific weather and climate event is the result of a complex mix of
20 human and natural factors. Instead, scientists quantify the relative importance of human and natural
21 influences on the magnitude and/or probability of specific extreme weather events. Such information is
22 important for disaster risk reduction planning, because improved knowledge about changes in the probability
23 and magnitude of relevant extreme events enables better quantification of disaster risks.
24
25 On a case-by-case basis, scientists can now quantify the contribution of human influences to the magnitude
26 and probability of many extreme events. This is done by estimating and comparing the probability or
27 magnitude of the same type of event between the current climate – including the increases in greenhouse gas
28 concentrations and other human influences – and an alternate world where the atmospheric greenhouse gases
29 remained at pre-industrial levels. FAQ 11.3 Figure 1 illustrates this approach using differences in
30 temperature and probability between the two scenarios as an example. Both the pre-industrial (blue) and
31 current (red) climates experience hot extremes, but with different probabilities and magnitudes. Hot extremes
32 of a given temperature have a higher probability of occurrence in the warmer current climate than in the
33 cooler pre-industrial climate. Additionally, an extreme hot event of a particular probability will be warmer in
34 the current climate than in the pre-industrial climate. Climate model simulations are often used to estimate
35 the occurrence of a specific event in both climates. The change in the magnitude and/or probability of the
36 extreme event in the current climate compared to the pre-industrial climate is attributed to the difference
37 between the two scenarios, which is the human influence.
38
39 Attributable increases in probability and magnitude have been identified consistently for many hot extremes.
40 Attributable increases have also been found for some extreme precipitation events, including hurricane
41 rainfall events, but these results can vary among events. In some cases, large natural variations in the climate
42 system prevent attributing changes in the probability or magnitude of a specific extreme to human influence.
43 Additionally, attribution of certain classes of extreme weather (e.g., tornadoes) is beyond current modelling
44 and theoretical capabilities. As the climate continues to warm, larger changes in probability and magnitude
45 are expected, and as a result it will be possible to attribute future temperature and precipitation extremes in
46 many locations to human influences. Attributable changes may emerge for other types of extremes as the
47 warming signal increases.
48
49 In conclusion, human-caused global warming has resulted in changes in a wide variety of recent extreme
50 weather events. Strong increases in probability and magnitude, attributable to human influence, have been
51 found for many heat waves and hot extremes around the world.
52
53
54 [START FAQ11.3 FIGURE 1 HERE]
55
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1 FAQ 11.3, Figure 1: Changes in climate result in changes in the magnitude and probability of extremes. Example of
2 how temperature extremes differ between a climate with pre-industrial greenhouse gases (shown
3 in blue) and the current climate (shown in orange) for a representative region. The horizontal
4 axis shows the range of extreme temperatures, while the vertical axis shows the annual chance of
5 each temperature event’s occurrence. Moving towards the right indicates increasingly hotter
6 extremes that are more rare (less probable). For hot extremes, an extreme event of a particular
7 temperature in the pre-industrial climate would be more probable (vertical arrow) in the current
8 climate. An event of a certain probability in the pre-industrial climate would be warmer
9 (horizontal arrow) in the current climate. While the climate under greenhouse gases at the pre-
10 industrial level experiences a range of hot extremes, such events are hotter and more frequent in
11 the current climate.
12
13 [END FAQ11.3 FIGURE 1 HERE]
14
15 [END FAQ11.3 HERE]
16
17
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1 Large tables
2 Color scale for tables for changes in temperature extremes and heavy precipitation
Fact Virtually Extremely Very Likely High Medium Low
certain likely likely confidence confidence confidence
Increasing hot
extremes, decreasing
cold extremes
Decreasing hot
extremes, increasing
cold extremes
Inconsistent sign
3
4 Color scale for tables for changes in droughts
Fact Virtually Extremely Very Likely High Medium Low
certain likely likely confidence confidence confidence
Increasing drought
Decreasing drought
Inconsistent sign
5
6 [START TABLE 11.4 HERE]
7
8 Table 11.4: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for temperature extremes in Africa,
9 subdivided by AR6 regions. See Sections 11.9.1 and 11.9.2 for details
All Africa Detection and attribution; Projections
Observed trends
event attribution 1.5 °C 2 °C 4 °C
Insufficient data for the Limited evidence for the CMIP6 models project a robust CMIP6 models project a CMIP6 models project a
continent, but there is high continent, but there is increase in the intensity and robust increase in the robust increase in the
confidence of an increase in medium confidence in a frequency of TXx events and a intensity and frequency of intensity and frequency of
the intensity and frequency of human contribution to the robust decrease in the intensity TXx events and a robust TXx events and a robust
hot extremes and decrease in observed increase in the and frequency of TNn events (Li decrease in the intensity and decrease in the intensity and
the intensity and frequency of intensity and frequency of et al., 2020; Annex). Median frequency of TNn events (Li frequency of TNn events (Li
cold extremes in all hot extremes and decrease increase of more than 0.5°C in et al., 2020; Annex). Median et al., 2020; Annex). Median
subregions with sufficient in the intensity and the 50-year TXx and TNn events increase of more than 1°C in increase of more than 3°C in
data frequency of cold compared to the 1°C warming the 50-year TXx and TNn the 50-year TXx and TNn
extremes for all level (Li et al., 2020) events compared to the 1°C events compared to the 1°C
subregions with sufficient warming level (Li et al., warming level ((Li et al.,
data 2020) 2020)
Medium confidence in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
increase in the intensity and human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
frequency of hot extremes observed increase in the Very likely (compared with the Extremely likely (compared Virtually certain (compared
and decrease in the intensity intensity and frequency of recent past (1995-2014)) with the recent past (1995- with the recent past (1995-
and frequency of cold hot extremes and decrease Extremely likely (compared with 2014)) 2014))
extremes. in the intensity and pre-industrial)
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frequency of cold Virtually certain (compared Virtually certain (compared
extremes. Decrease in the intensity and with pre-industrial) with pre-industrial)
frequency of cold extremes:
Very likely (compared with the Decrease in the intensity and Decrease in the intensity and
recent past (1995-2014)) frequency of cold extremes: frequency of cold extremes:
Extremely likely (compared with Extremely likely (compared Virtually certain (compared
pre-industrial) with the recent past (1995- with the recent past (1995-
2014)) 2014))
Virtually certain (compared Virtually certain (compared
with pre-industrial) with pre-industrial)
2 Significant ncreases in the Robust evidence of a CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
Mediterranean (MED)
intensity and frequency of hot human contribution to the increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant observed increase in the frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and intensity and frequency of robust decrease in the intensity and robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes hot extremes and decrease frequency of TNn events (Li et al., and frequency of TNn events and frequency of TNn events
(Peña-Angulo et al., 2020; El in the intensity and 2020; Annex). Median increase of (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Kenawy et al., 2013; Acero et more than 0.5°C in the 50-year TXx Median increase of more than Median increase of more than
frequency of cold
al., 2014; Fioravanti et al., and TNn events compared to the 1°C 1°C in the 50-year TXx and 3.5°C in the 50-year TXx and
extremes (Seong et al.,
2016; Ruml et al., 2017; warming level (Li et al., 2020) and TNn events compared to the TNn events compared to the
Türkeş and Erlat, 2018; Donat 2020; Wang et al., 2017; more than 2°C in annual TXx and 1°C warming level (Li et al., 1°C warming level (Li et al.,
et al., 2013, 2014, 2016; Sippel and Otto, 2014; TNn compared to pre-industrial 2020) and more than 2.5°C in 2020) and more than 5°C in
Filahi et al., 2016; Driouech Wilcox et al., 2018) (Annex). annual TXx and TNn compred annual TXx and TNn compared
et al., 2021; Dunn et al., to pre-industrial (Annex). to pre-industrial (Annex).
2020) Additional evidence from CMIP5
and RCM simulations for an increase Additional evidence from Additional evidence from
in the intensity and frequency of hot CMIP5 and RCM simulations CMIP5 and RCM simulations
for an increase in the intensity for an increase in the intensity
extremes and decrease in the
and frequency of hot extremes
intensity and frequency of cold and frequency of hot extremes
and decrease in the intensity
extremes (Cardoso et al., 2019; Zollo and decrease in the intensity and frequency of cold extremes
et al., 2016; Weber et al., 2018) and frequency of cold extremes (Cardoso et al., 2019; Nastos
(Cardoso et al., 2019; Tomozeiu and Kapsomenakis, 2015;
et al., 2014; Abaurrea et al., Tomozeiu et al., 2014; Cardell
2018; Nastos and et al., 2020; Zollo et al., 2016;
Kapsomenakis, 2015; Cardell Giorgi et al., 2014; Driouech et
al., 2020; Coppola et al., 2021a;
et al., 2020; Zollo et al., 2016;
Engelbrecht et al., 2015)
Weber et al., 2018; Coppola et
al., 2021a)
2
This region includes both northern Africa and southern Europe
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Very likely increase in the Human influence likely Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of contributed to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
hot extremes and decrease observed increase in Likely (compared with the recent Very likely (compared with the Virtually certain (compared
in the intensity and the intensity and past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
frequency of cold extremes frequency of hot Very likely (compared with pre- Extremely likely (compared 2014))
extremes and decrease industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
in the intensity and
Decrease in the intensity and Decrease in the intensity and
frequency of cold frequency of cold extremes: Decrease in the intensity and
frequency of cold extremes:
extremes Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial) with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Sahara (SAH) Significant increases in the Strong evidence of CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot changes from increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant observations that are in frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and the direction of model robust decrease in the intensity and robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes projected changes for the frequency of TNn events (Li et al., and frequency of TNn events and frequency of TNn events
(Donat et al., 2014a; Moron et future. The magnitude of 2020; Annex). Median increase of (Li et al., 2020; Annex). (Li et al., 2020; Annex).
al., 2016; Dunn et al., 2020) projected changes more than 0.5°C in the 50-year TXx Median increase of more than Median increase of more than
increases with global and TNn events compared to the 1°C 1°C in the 50-year TXx and 3.5°C in the 50-year TXx and
warming. warming level (Li et al., 2020) and TNn events compared to the TNn events compared to the
more than 2°C in annual TXx and 1°C warming level (Li et al., 1°C warming level (Li et al.,
TNn compared to pre-industrial 2020 ) and more than 2.5°C in 2020) and more than 5°C in
(Annex). annual TXx and TNn compared annual TXx and TNn compared
to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from CMIP5
and CORDEX simulations for an Additional evidence from Additional evidence from
increase in the intensity and CMIP5 and CORDEX CMIP5/CMIP3 and CORDEX
frequency of hot extremes (Weber et simulations for an increase in simulations for an increase in
al., 2018) the intensity and frequency of the intensity and frequency of
hot extremes (Weber et al., hot extremes (Coppola et al.,
2018; Coppola et al., 2021a) 2021a; Engelbrecht et al., 2015;
Giorgi et al., 2014)
Likely increase in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of intensity and frequency of past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes hot extremes and decrease Very likely (compared with pre- Extremely likely (compared 2014))
in the intensity and industrial) with pre-industrial) Virtually certain (compared
frequency of cold with pre-industrial)
extremes. Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
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Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Western Africa (WAF) Significant increases in the Strong evidence of CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot changes from increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant observations that are in frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and the direction of model robust decrease in the intensity and robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes projected changes for the frequency of TNn events (Li et al., and frequency of TNn events and frequency of TNn events
(Barry et al., 2018; Chaney et future. The magnitude of 2020; Annex). Median increase of (Li et al., 2020; Annex). (Li et al., 2020; Annex).
al., 2014; Dunn et al., 2020; projected changes more than 0.5°C in the 50-year TXx Median increase of more than Median increase of more than
Mouhamed et al., 2013; increases with global and TNn events compared to the 1°C in the 50-year TXx and 3°C in the 50-year TXx and
Perkins-Kirkpatrick and warming. 1°C warming level (Li et al., 2020) TNn events compared to the TNn events compared to the
Lewis, 2020) and more than 1.5°C in annual TXx 1°C warming level (Li et al., 1°C warming level (Li et al.,
and TNn compared to pre-industrial 2020) and more than 2°C in 2020) and more than 4.5°C in
(Annex). annual TXx and TNn compared annual TXx and TNn compared
to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from CMIP5
and CORDEX simulations for an Additional evidence from Additional evidence from
increase in the intensity and CMIP5 and CORDEX CMIP5/CMIP3 and CORDEX
frequency of hot extremes (Weber simulations for an increase in simulations for an increase in
et al., 2018) the intensity and frequency of the intensity and frequency of
hot extremes (Weber et al., hot extremes (Coppola et al.,
2018; Coppola et al., 2021a) 2021a; Engelbrecht et al., 2015;
Giorgi et al., 2014)
Likely increase in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of intensity and frequency of past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes hot extremes and decrease Very likely (compared with pre- Extremely likely (compared 2014))
in the intensity and industrial) with pre-industrial) Virtually certain (compared
frequency of cold with pre-industrial)
extremes. Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Northern Eastern Africa Increases in the intensity and Evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
(NEAF) frequency of hot extremes contribution to the increase in the intensity and increase in the intensity and increase in the intensity and
and decreases in the intensity observed increase in the frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
and frequency of cold intensity and frequency of robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
extremes (Perkins-Kirkpatrick hot extremes and decrease and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
and Lewis, 2020; Chaney et in the intensity and et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
al., 2014; Gebrechorkos et al., frequency of cold increase of more than 0.5°C in Median increase of more than Median increase of more than
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2018; Omondi et al., 2014; extremes (Otto et al., the 50-year TXx and TNn events 1°C in the 50-year TXx and 2.5°C in the 50-year TXx and
Dunn et al., 2020) 2015; Philip et al., 2020; compared to the 1°C warming TNn events compared to the TNn events compared to the
Marthews et al., 2015; level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
Kew et al., 2021; Funk et than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 4°C in
al., 2015) TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from CMIP5 Additional evidence from Additional evidence from
and CORDEX simulations for an CMIP5 and CORDEX CMIP5/CMIP3 and CORDEX
increase in the intensity and simulations for an increase in simulations for an increase in
frequency of hot extremes the intensity and frequency of the intensity and frequency of
(Weber et al., 2018) hot extremes (Weber et al., hot extremes Coppola et al.,
2018; Coppola et al., 2021a) 2021a; Engelbrecht et al., 2015;
Giorgi et al., 2014)
Medium confidence in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
increase in the intensity and human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
frequency of hot extremes observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
hot extremes Very likely (compared with pre- Extremely likely (compared 2014))
industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Central Africa (CAF) Insufficient data to assess Limited evidence CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
trends (Dunn et al., 2020) increase in the intensity and increase in the intensity and increase in the intensity and
frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
increase of more than 0C in the Median increase of more than Median increase of more than
50-year TXx and TNn events 1°C in the 50-year TXx and 3°C in the 50-year TXx and
compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 4.5°C in
TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from CMIP5 Additional evidence from Additional evidence from
and CORDEX simulations for an CMIP5 and CORDEX CMIP5/CMIP3 and CORDEX
increase in the intensity and simulations for an increase in simulations for an increase in
the intensity and frequency of the intensity and frequency of
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frequency of hot extremes hot extremes (Weber et al., hot extremes (Coppola et al.,
(Weber et al., 2018) 2018; Coppola et al., 2021a) 2021a; Engelbrecht et al., 2015;
Giorgi et al., 2014)
Low confidence Low confidence Increase in the intensity and Increase in the intensity and Increase in the intensity and
frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
Likely (compared with the recent Very likely (compared with the Virtually certain (compared
past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
Very likely (compared with pre- Extremely likely (compared 2014))
industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
South Eastern Africa (SEAF) Increases in the intensity and Evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
frequency of hot extremes contribution to the increase in the intensity and increase in the intensity and increase in the intensity and
and decreases in the intensity observed increase in the frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
and frequency of cold intensity and frequency of robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
extremes (Perkins-Kirkpatrick hot extremes and decrease and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
and Lewis, 2020; in the intensity and et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Gebrechorkos et al., 2018; frequency of cold increase of more than 0.5°C in Median increase of more than Median increase of more than
Omondi et al., 2014; Chaney extremes (Otto et al., the 50-year TXx and TNn events 1°C in the 50-year TXx and 2.5°C in the 50-year TXx and
et al., 2014) 2015; Philip et al., 2020; compared to the 1°C warming TNn events compared to the TNn events compared to the
Marthews et al., 2015; level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
Kew et al., 2021; Funk et than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 4°C in
al., 2015) TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from CMIP5 Additional evidence from Additional evidence from
and CORDEX simulations for an CMIP5 and CORDEX CMIP5/CMIP3 and CORDEX
increase in the intensity and simulations for an increase in simulations for an increase in
frequency of hot extremes the intensity and frequency of the intensity and frequency of
(Weber et al., 2018) hot extremes (Weber et al., hot extremes (Coppola et al.,
2018; Coppola et al., 2021a) 2021a; Engelbrecht et al., 2015;
Giorgi et al., 2014)
Medium confidence in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
increase in the intensity and human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
frequency of hot extremes observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
hot extremes Very likely (compared with pre- Extremely likely (compared 2014))
industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
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Decrease in the intensity and Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: frequency of cold extremes:
Likely (compared with the recent Very likely (compared with the Virtually certain (compared
past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
Very likely (compared with pre- Extremely likely (compared 2014))
industrial). with pre-industrial) Virtually certain (compared
with pre-industrial)
Western Southern Africa Significant increases in the Robust evidence of a CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
(WSAF) intensity and frequency of hot human contribution to the increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant observed increase in the frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and intensity and frequency of robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes hot extremes and decrease and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Russo et al., 2016; Perkins- in the intensity and et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Kirkpatrick and Lewis, 2020; frequency of cold increase of more than 0.5°C in Median increase of more than Median increase of more than
Kruger and Nxumalo, 2017; extremes (Seong et al., the 50-year TXx and TNn events 1°C in the 50-year TXx and 2.5°C in the 50-year TXx and
Mbokodo et al., 2020; Dunn 2020; Wang et al., 2017) compared to the 1°C warming TNn events compared to the TNn events compared to the
et al., 2020) level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 4.5°C in
TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from CMIP5 Additional evidence from Additional evidence from
and CORDEX simulations for an CMIP5 and CORDEX CMIP5/CMIP3 and CORDEX
increase in the intensity and simulations for an increase in simulations for an increase in
frequency of hot extremes the intensity and frequency of the intensity and frequency of
(Weber et al., 2018) hot extremes (Weber et al., hot extremes (Coppola et al.,
2018; Coppola et al., 2021a) 2021a; Engelbrecht et al., 2015;
Giorgi et al., 2014)
Likely increase in the Human influence likely Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contributed to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of intensity and frequency of past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes hot extremes and decrease Very likely (compared with pre- Extremely likely (compared 2014))
in the intensity and industrial) with pre-industrial) Virtually certain (compared
frequency of cold with pre-industrial)
extremes Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Eastearn Southern Africa Significant increases in the Robust evidence of a CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
(ESAF) intensity and frequency of hot human contribution to the increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant observed increase in the frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and intensity and frequency of robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
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Final Government Distribution Chapter11 IPCC AR6 WGI
frequency of cold extremes hot extremes and decrease and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Dunn et al., 2020; Russo et in the intensity and et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
al., 2016; Perkins-Kirkpatrick frequency of cold increase of more than 0.5°C in Median increase of more than Median increase of more than
and Lewis, 2020; Kruger and extremes (Seong et al., the 50-year TXx and TNn events 0.5°C in the 50-year TXx and 2.5°C in the 50-year TXx and
Nxumalo, 2017; Mbokodo et 2020; Wang et al., 2017) compared to the 1°C warming TNn events compared to the TNn events compared to the
al., 2020) level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 4°C in
TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from CMIP5 Additional evidence from Additional evidence from
and CORDEX simulations for an CMIP5 and CORDEX CMIP5/CMIP3 and CORDEX
increase in the intensity and simulations for an increase in simulations for an increase in
frequency of hot extremes the intensity and frequency of the intensity and frequency of
(Weber et al., 2018) hot extremes (Weber et al., hot extremes (Coppola et al.,
2018; Coppola et al., 2021a) 2021a; Engelbrecht et al., 2015;
Giorgi et al., 2014)
Likely increase in the High confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of intensity and frequency of past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes hot extremes and decrease Very likely (compared with pre- Extremely likely (compared 2014))
in the intensity and industrial) with pre-industrial) Virtually certain (compared
frequency of cold with pre-industrial)
extremes Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Madagascar (MDG) Increases in the intensity and Limited evidence CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
frequency of hot extremes increase in the intensity and increase in the intensity and increase in the intensity and
and decreases in the intensity frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
and frequency of cold robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
extremes (Vincent et al., and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
2011; Donat et al., 2013) et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
increase of more than 0.5°C in Median increase of more than Median increase of more than
the 50-year TXx and TNn events 0.5°C in the 50-year TXx and 2°C in the 50-year TXx and
compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 3.5°C in
TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
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Final Government Distribution Chapter11 IPCC AR6 WGI
Additional evidence from CMIP5 Additional evidence from Additional evidence from
and CORDEX simulations for an CMIP5 and CORDEX CMIP5/CMIP3 and CORDEX
increase in the intensity and simulations for an increase in simulations for an increase in
frequency of hot extremes the intensity and frequency of the intensity and frequency of
(Weber et al., 2018) hot extremes (Weber et al., hot extremes (Coppola et al.,
2018; Coppola et al., 2021a) 2021a; Engelbrecht et al., 2015;
Giorgi et al., 2014)
Medium confidence in the Low confidence Increase in the intensity and Increase in the intensity and Increase in the intensity and
increase in the intensity and frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
frequency of hot extremes Likely (compared with the recent Very likely (compared with the Virtually certain (compared
and decrease in the intensity past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
and frequency of cold Very likely (compared with pre- Extremely likely (compared 2014))
extremes industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
1
2 [END TABLE 11.4 HERE]
3
4 [START TABLE 11.5 HERE]
5
6 Table 11.5: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for heavy precipitation in Africa, subdivided
7 by AR6 regions. See Sections 11.9.1 and 11.9.3 for details.
Region Observed trends Detection and attribution; Projections
event attribution 1.5 °C 2 °C 4 °C
All Africa Insufficient data to assess trends Limited evidence CMIP6 models project an increase in CMIP6 models project a robust increase in the CMIP6 models project a
the intensity and frequency of heavy intensity and frequency of heavy precipitation robust increase in the
precipitation (Li et al., 2020a). (Li et al., 2020a). Median increase of more than intensity and frequency of
Median increase of more than 2% in 6% in the 50-year Rx1day and Rx5day events heavy precipitation (Li et al.,
the 50-year Rx1day and Rx5day compared to the 1°C warming level (Li et al., 2020a). Median increase of
events compared to the 1°C warming 2020a) more than 20% in the 50-
level (Li et al., 2020a) year Rx1day and Rx5day
events compared to the 1°C
warming level (Li et al.,
2020a)
Low confidence Low confidence Intensification of heavy precipitation: Intensification of heavy precipitation: Intensification of heavy
High confidence (compared with the Likely (compared with the recent past (1995- precipitation:
recent past (1995-2014)) 2014))
Likely (compared with pre-industrial) Very likely (compared with pre-industrial)
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Final Government Distribution Chapter11 IPCC AR6 WGI
Extremely likely (compared
with the recent past (1995-
2014))
Virtually certain (compared
with pre-industrial)
3 Lack of agreement on the Limited evidence (Añel et CMIP6 models, CMIP5 models, and CMIP6 models project a robust increase in the CMIP6 models project a
Mediterranean (MED)
evidence of trends (Sun et al., al., 2014; U.S. Department RCMs project inconsistent changes in intensity and frequency of heavy precipitation (Li robust increase in the intensity
2020; Casanueva et al., 2014; de of Agriculture Economic the region (Li et al., 2020; Cardell et et al., 2020; Annex). Median increase of more and frequency of heavy
Lima et al., 2015; Gajić-Čapka et Research Service, 2016) al., 2020; Zollo et al., 2016; Samuels than 2% in the 50-year Rx1day and Rx5day precipitation (Li et al., 2020;
al., 2015; Ribes et al., 2019; et al., 2018) events compared to the 1°C warming level (Li et Annex). Median increase of
Peña-Angulo et al., 2020; al., 2020a) and more than 0% in annual Rx1day more than 8% in the 50-year
Rajczak and Schär, 2017; and Rx5day and less than -2% in annual Rx30day Rx1day and Rx5day events
Jacob et al., 2018; Coppola et al., compared to pre-industrial (Annex). compared to the 1°C warming
2021a; Donat et al., 2014; level (Li et al., 2020a) and
Mathbout et al., 2018; Dunn et Additional evidence from CMIP5 and RCM more than 2% in annual
al., 2020) simulations for an increase in the intensity of Rx1day and Rx5day and less
heavy precipitation (Cardell et al., 2020; Zollo et than -2% in annual Rx30day
al., 2016; Samuels et al., 2018) compared to pre-industrial
(Annex).
Additional evidence from
CMIP5 and RCM simulations
for an increase in the intensity
of heavy precipitation (Cardell
et al., 2020; Tramblay and
Somot, 2018; Zollo et al.,
2016; Samuels et al., 2018;
Monjo et al., 2016; Rajczak et
al., 2013; Coppola et al.,
2021b; Driouech et al., 2020)
Low confidence Low confidence Intensification of heavy precipitation: Intensification of heavy precipitation: Intensification of heavy
Low confidence (compared with the Medium confidence (compared with the recent precipitation:
recent past (1995-2014)) past (1995-2014)) High confidence (compared
Medium confidence (compared with High confidence (compared with pre-industrial) with the recent past (1995-
pre-industrial) 2014))
High confidence (compared
with pre-industrial)
Sahara (SAH) Insufficient data to assess trends Limited evidence CMIP6 models project an increase in the CMIP6 models project a robust increase in the CMIP6 models project a
(Sun et al., 2020; Dunn et al., intensity and frequency of heavy intensity and frequency of heavy precipitation (Li robust increase in the intensity
2020) precipitation (Li et al., 2020; Annex). et al., 2020; Annex). Median increase of more and frequency of heavy
Median increase of more than 4% in the than 8% in the 50-year Rx1day and Rx5day precipitation (Li et al., 2020;
50-year Rx1day and Rx5day events events compared to the 1°C warming level (Li et Annex). Median increase of
compared to the 1°C warming level (Li al., 2020a) and more than 20% in annual Rx1day, more than 30% in the 50-year
et al., 2020a) and more than 15% in Rx1day and Rx5day events
3
This region includes both northern Africa and southern Europe
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Final Government Distribution Chapter11 IPCC AR6 WGI
annual Rx1day, Rx5day, and Rx30day Rx5day, and Rx30day compared to pre-industrial compared to the 1°C warming
compared to pre-industrial (Annex). (Annex). level (Li et al., 2020a) and
more than 40% in annual
Rx1day, Rx5day, and
Rx30day compared to pre-
industrial (Annex).
Low confidence Low confidence Intensification of heavy precipitation: Intensification of heavy precipitation: Intensification of heavy
High confidence (compared with the Likely (compared with the recent past (1995- precipitation:
recent past (1995-2014)) 2014)) Extremely likely (compared
Likely (compared with pre-industrial) Very likely (compared with pre-industrial) with the recent past (1995-
2014))
Virtually certain (compared
with pre-industrial)
Western Africa (WAF) Insufficient data and a lack of Limited evidence (Parker CMIP6 models project an increase in the CMIP6 models project a robust increase in the CMIP6 models project a
agreement on the evidence of et al., 2017) intensity and frequency of heavy intensity and frequency of heavy precipitation (Li
robust increase in the intensity
trends (Mouhamed et al., precipitation (Li et al., 2020; Annex). et al., 2020; Annex). Median increase of more and frequency of heavy
2013; Chaney et al., 2014; Median increase of more than 4% in the than 8% in the 50-year Rx1day and Rx5day precipitation (Li et al., 2020;
Sanogo et al., 2015; Zittis, 50-year Rx1day and Rx5day events events compared to the 1°C warming level (Li et Annex). Median increase of
2017; Barry et al., 2018; Sun et compared to the 1°C warming level (Li al., 2020a) and more than 15% in annual Rx1day more than 25% in the 50-year
al., 2020; Dunn et al., 2020) et al., 2020a) and more than 10% in and Rx5day and 10% in annual Rx30day Rx1day and Rx5day events
annual Rx1day and Rx5day and 8% in compared to pre-industrial (Annex). compared to the 1°C warming
annual Rx30day compared to pre- level (Li et al., 2020a) and
industrial (Annex). Additional evidence from CMIP5 and CORDEX more than 30% in annual
simulations for an increase in the intensity of Rx1day and Rx5day and 15%
Additional evidence from CMIP5 and heavy precipitation (Nikulin et al., 2018; Déqué et in annual Rx30day compared
CORDEX simulations for an increase in al., 2017) to pre-industrial (Annex).
the intensity of heavy precipitation
(Nikulin et al., 2018) Additional evidence from
CMIP5 and CORDEX
simulations for an increase in
the intensity of heavy
precipitation (Giorgi et al.,
2014; Dosio et al., 2019;
Akinsanola and Zhou, 2018;
Coppola et al., 2021b)
Low confidence Low confidence Intensification of heavy precipitation: Intensification of heavy precipitation: Intensification of heavy
High confidence (compared with the Likely (compared with the recent past (1995- precipitation:
recent past (1995-2014)) 2014)) Extremely likely (compared
Likely (compared with pre-industrial) Very likely (compared with pre-industrial) with the recent past (1995-
2014))
Virtually certain (compared
with pre-industrial)
North Eastern Africa (NEAF) Insufficient data to assess trends Limited evidence CMIP6 models project an increase in the CMIP6 models project a robust increase in the CMIP6 models project a
(Sun et al., 2020; Dunn et al., intensity and frequency of heavy intensity and frequency of heavy precipitation (Li robust increase in the intensity
2020) precipitation (Li et al., 2020; Annex). et al., 2020; Annex). Median increase of more and frequency of heavy
Median increase of more than 4% in the than 8% in the 50-year Rx1day and Rx5day precipitation (Li et al., 2020;
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Final Government Distribution Chapter11 IPCC AR6 WGI
50-year Rx1day and Rx5day events events compared to the 1°C warming level (Li et Annex). Median increase of
compared to the 1°C warming level (Li al., 2020a) and more than 10% in annual Rx1day, more than 25% in the 50-year
et al., 2020a) and more than 8% in Rx5day, and Rx30day compared to pre-industrial Rx1day and Rx5day events
annual Rx1day and Rx5day and 6% in (Annex). compared to the 1°C warming
annual Rx30day compared to pre- level (Li et al., 2020a) and
industrial (Annex). more than 35% in annual
Rx1day and Rx5day and 30%
in annual Rx30day compared
to pre-industrial (Annex).
Low confidence Low confidence Intensification of heavy precipitation: Intensification of heavy precipitation: Intensification of heavy
High confidence (compared with the Likely (compared with the recent past (1995- precipitation:
recent past (1995-2014)) 2014)) Extremely likely (compared
Likely (compared with pre-industrial) Very likely (compared with pre-industrial) with the recent past (1995-
2014))
Virtually certain (compared
with pre-industrial)
Central Africa (CAF) Insufficient data to assess trends Limited evidence (Otto et CMIP6 models project an increase in the CMIP6 models project a robust increase in the CMIP6 models project a
(Sun et al., 2020; Dunn et al., al., 2013) intensity and frequency of heavy intensity and frequency of heavy precipitation (Li
robust increase in the intensity
2020) precipitation (Li et al., 2020; Annex). et al., 2020; Annex). Median increase of more and frequency of heavy
Median increase of more than 2% in the than 6% in the 50-year Rx1day and Rx5day precipitation (Li et al., 2020;
50-year Rx1day and Rx5day events events compared to the 1°C warming level (Li et Annex). Median increase of
compared to the 1°C warming level (Li al., 2020a) and more than 10% in annual Rx1day, more than 20% in the 50-year
et al., 2020a) and more than 10% in Rx5day, and Rx30day compared to pre-industrial Rx1day and Rx5day events
annual Rx1day and Rx5day and 8% in (Annex). compared to the 1°C warming
annual Rx30day compared to pre- level (Li et al., 2020a) and
industrial (Annex). Additional evidence from CMIP5 and CORDEX more than 30% in annual
simulations for an increase in the intensity of Rx1day and Rx5day and 20%
Additional evidence from CMIP5 and heavy precipitation (Nikulin et al., 2018; Déqué et in annual Rx30day compared
CORDEX simulations for an increase in al., 2017; Coppola et al., 2021b) to pre-industrial (Annex).
the intensity of heavy precipitation
(Nikulin et al., 2018) Additional evidence from
CMIP5 and CORDEX
simulations for an increase in
the intensity of heavy
precipitation (Diedhiou et al.
2018; Fotso-Nguemo et al.
2018; Sonkoué et al. 2019;
Coppola et al., 2021b)
Low confidence Low confidence Intensification of heavy precipitation: Intensification of heavy precipitation: Intensification of heavy
High confidence (compared with the Likely (compared with the recent past (1995- precipitation:
recent past (1995-2014)) 2014)) Extremely likely (compared
Likely (compared with pre-industrial) Very likely (compared with pre-industrial) with the recent past (1995-
2014))
Virtually certain (compared
with pre-industrial)
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Final Government Distribution Chapter11 IPCC AR6 WGI
South Eastern Africa (SEAF) Insufficient data to assess trends Limited evidence CMIP6 models project an increase in the CMIP6 models project a robust increase in the CMIP6 models project a
(Sun et al., 2020; Dunn et al., intensity and frequency of heavy intensity and frequency of heavy precipitation (Li robust increase in the intensity
2020) precipitation (Li et al., 2020; Annex). et al., 2020; Annex). Median increase of more and frequency of heavy
Median increase of more than 2% in the than 4% in the 50-year Rx1day and Rx5day precipitation (Li et al., 2020;
50-year Rx1day and Rx5day events events compared to the 1°C warming level (Li et Annex). Median increase of
compared to the 1°C warming level (Li al., 2020a) and more than 8% in annual Rx1day more than 15% in the 50-year
et al., 2020a) and more than 6% in and Rx5day and 6% in annual Rx30day compared Rx1day and Rx5day events
annual Rx1day and Rx5day and 4% in to pre-industrial (Annex). compared to the 1°C warming
annual Rx30day compared to pre- level (Li et al., 2020a) and
industrial (Annex). more than 25% in annual
Rx1day and Rx5day and 15%
in annual Rx30day compared
to pre-industrial (Annex).
Low confidence Low confidence Intensification of heavy precipitation: Intensification of heavy precipitation: Intensification of heavy
High confidence (compared with the Likely (compared with the recent past (1995- precipitation:
recent past (1995-2014)) 2014)) Extremely likely (compared
Likely (compared with pre-industrial) Very likely (compared with pre-industrial) with the recent past (1995-
2014))
Virtually certain (compared
with pre-industrial)
West Southern Africa Intensification of heavy Limited evidence CMIP6 models project inconsistent CMIP6 models project inconsistent changes in the CMIP6 models project an
(WSAF) precipitation (Sun et al., 2020; changes in the region (Li et al., 2020, region (Li et al., 2020, Annex) increase in the intensity and
Donat et al., 2013) Annex) frequency of heavy
precipitation (Li et al., 2020;
Annex). Median increase of
more than 10% in the 50-year
Rx1day and Rx5day events
compared to the 1°C warming
level (Li et al., 2020a) and
more than 4% in annual
Rx1day and Rx5day and 0% in
annual Rx30day compared to
pre-industrial (Annex).
Additional evidence from
CMIP5 and RCM simulations
for an increase in the intensity
of heavy precipitation (Pinto et
al., 2016; Dosio et al., 2019;
Giorgi et al., 2014; Coppola et
al., 2021b)
Medium confidence in the Low confidence Intensification of heavy precipitation: Intensification of heavy precipitation: Intensification of heavy
intensification of heavy Low confidence (compared with the Low confidence (compared with the recent past precipitation:
precipitation. recent past (1995-2014)) (1995-2014)) High confidence (compared
Low confidence (compared with pre- Medium confidence (compared with pre- with the recent past (1995-
industrial) industrial) 2014))
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Final Government Distribution Chapter11 IPCC AR6 WGI
Likely (compared with pre-
industrial)
East Southern Africa (ESAF) Intensification of heavy Limited evidence CMIP6 models project an increase in the CMIP6 models project an increase in the intensity CMIP6 models project a
precipitation (Sun et al., 2020; intensity and frequency of heavy and frequency of heavy precipitation (Li et al., robust increase in the intensity
Donat et al., 2013) precipitation (Li et al., 2020; Annex). 2020; Annex). Median increase of more than 2% and frequency of heavy
Median increase of more than 2% in the in the 50-year Rx1day and Rx5day events precipitation (Li et al., 2020;
50-year Rx1day and Rx5day events compared to the 1°C warming level (Li et al., Annex). Median increase of
compared to the 1°C warming level (Li 2020a) and more than 6% in annual Rx1day and more than 15% in the 50-year
et al., 2020a) and more than 4% in Rx5day and 2% in annual Rx30day compared to Rx1day and Rx5day events
annual Rx1day and Rx5day and 0% in pre-industrial (Annex). compared to the 1°C warming
annual Rx30day compared to pre- level (Li et al., 2020a) and
industrial (Annex). more than 15% in annual
Rx1day and Rx5day and 8% in
annual Rx30day compared to
pre-industrial (Annex).
Additional evidence from
CMIP5 and RCM simulations
for an increase in the intensity
of heavy precipitation (Pinto et
al., 2016; Dosio et al., 2019;
Giorgi et al., 2014; Coppola et
al., 2021b)
Medium confidence in the Low confidence Intensification of heavy precipitation: Intensification of heavy precipitation: Intensification of heavy
intensification of heavy Medium confidence (compared with the High confidence (compared with the recent past precipitation:
precipitation. recent past (1995-2014)) (1995-2014)) Very likely (compared with the
High confidence (compared with pre- Likely (compared with pre-industrial) recent past (1995-2014))
industrial) Extremely likely (compared
with pre-industrial)
Madagascar (MDG) Insufficient data to assess trends Limited evidence CMIP6 models project an increase in CMIP6 models project an increase in the CMIP6 models project a
and trends in available data are the intensity and frequency of heavy intensity and frequency of heavy precipitation robust increase in the
not significant (Sun et al., 2020; precipitation (Li et al., 2020; Annex). (Li et al., 2020; Annex). Median increase of intensity and frequency of
Dunn et al., 2020; Donat et al., Median increase of more than 2% in more than 2% in the 50-year Rx1day and heavy precipitation (Li et al.,
2013; Vincent et al., 2011) the 50-year Rx1day and Rx5day Rx5day events compared to the 1°C warming 2020; Annex). Median
events compared to the 1°C warming level (Li et al., 2020a) and more than 4% in increase of more than 15%
level (Li et al., 2020a) and more than annual Rx1day and Rx5day and 2% in annual in the 50-year Rx1day and
4% in annual Rx1day and Rx5day and Rx30day compared to pre-industrial (Annex). Rx5day events compared to
0% in annual Rx30day compared to the 1°C warming level (Li et
pre-industrial (Annex). Additional evidence from CMIP5 and al., 2020a) and more than
CORDEX simulations for an increase in the 15% in annual Rx1day and
Additional evidence from CMIP5 and intensity of heavy precipitation (Weber et al., Rx5day and 6% in annual
CORDEX simulations for an increase 2018) Rx30day compared to pre-
in the intensity of heavy precipitation industrial (Annex).
(Weber et al., 2018)
Do Not Cite, Quote or Distribute 11-135 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
Additional evidence from
CMIP5 and CORDEX
simulations for an increase
in the intensity of heavy
precipitation (Weber et al.,
2018)
Low confidence Low confidence Intensification of heavy precipitation: Intensification of heavy precipitation: Intensification of heavy
Medium confidence (compared with High confidence (compared with the recent past precipitation:
the recent past (1995-2014)) (1995-2014)) Very likely (compared with
High confidence (compared with pre- Likely (compared with pre-industrial) the recent past (1995-2014))
industrial) Extremely likely (compared
with pre-industrial)
1
2 [END TABLE 11.5 HERE]
3
4
5 [START TABLE 11.6 HERE]
6
7 Table 11.6: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for meteorological droughts (MET),
8 agricultural and ecological droughts (AGR/ECOL), and hydrological droughts (HYDR) in Africa, subdivided by AR6 regions. See Sections 11.9.1 and 11.9.4 for
9 details.
Region and drought Projections
Observed trends Human contribution
type 1.5 °C 2 °C 4 °C
4 MET ENTRY IDENTICAL TO ENTRY IDENTICAL TO ENTRY IDENTICAL TO EU-MED ENTRY IDENTICAL TO EU-MED ENTRY IDENTICAL TO EU-MED
MED
EU-MED EU-MED
AGR ENTRY IDENTICAL TO ENTRY IDENTICAL TO ENTRY IDENTICAL TO EU-MED ENTRY IDENTICAL TO EU-MED ENTRY IDENTICAL TO EU-MED
ECOL EU-MED EU-MED
HYDR ENTRY IDENTICAL TO ENTRY IDENTICAL TO ENTRY IDENTICAL TO EU-MED ENTRY IDENTICAL TO EU-MED ENTRY IDENTICAL TO EU-MED
EU-MED EU-MED
Sahara MET Low confidence: Limited Low confidence: Limited Low confidence: Mixed signals (seasonally Low confidence: Mixed signals (seasonally Low confidence: Mixed signals
(SAH) evidence. evidence and geographically varying) and non-robust and geographically varying) and non-robust (seasonally and geographically varying)
changes (Cook et al., 2020). Slightly reduced changes (Cook et al., 2020). Slightly reduced and non-robust changes (Cook et al.,
drying based on CDD (Chapter 11 drying based on CDD (Chapter 11 2020). Reduced drying based on CDD
Supplementary Material (11.SM)). Supplementary Material (11.SM)). (Chapter 11 Supplementary Material
(11.SM)).
AGR Low confidence; Limited Low confidence; Limited Low confidence: Limited evidence and Low confidence: Limited evidence and Low confidence: Limited evidence and
ECOL evidence. evidence. inconsistent signals in CMIP6 (Chapter 11 inconsistent signals in CMIP6 (Chapter 11 inconsistent signals in CMIP6. (Cook et
Supplementary Material (11.SM)). Supplementary Material (11.SM)). al., 2020; Vicente-Serrano et al.,
4
This region includes both northern Africa and southern Europe
Do Not Cite, Quote or Distribute 11-136 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
2020a)(Chapter 11 Supplementary
Material (11.SM))
HYDR Low confidence: Limited Low confidence: Limited Low confidence: Limited evidence. One Low confidence: Limited evidence. One Low confidence: Inconsistent trends
evidence evidence study shows lack of signal (Touma et al., study shows lack of signal (Touma et al., (Touma et al., 2015; Cook et al., 2020)
2015) 2015)
Western MET Medium confidence: Low confidence: Mixed Low confidence: Mixed signal. Mean Low confidence: Mixed signal. Mean Medium confidence: Increase in
Africa Increased drying based on signals (Lawal et al., 2016; increase of CDD over larger part of Guinea increase of CDD over larger part of Guinea meteorological droughts, mostly on
(WAF) CDD and SPI (Chaney et Knutson and Zeng, 2018). Coast in 25 CORDEX AFR runs, 1.5°C Coast in 25 CORDEX AFR runs, 1.5°C seasonal time scale. Seasonal CDD
al., 2014; Barry et al., minus 1971-2000 (Klutse et al., 2016); slight minus 1971-2000 (Klutse et al., 2016); slight increases in the region for MAM and JJA
2018; Spinoni et al., Drying attributable in
2019; Dunn et al., 2020) fraction of region to climate increase in SPI-based meteorological increase in SPI-based meteorological (Dosio et al., 2019), increase in SPI-based
change over 1901-2010 and drought frequency and magntidue in the drought frequency and magntidue in the meteorological drought frequency and
1951-2010 time frames, but Niger and Volta river basin in CORDEX Niger and Volta river basin in CORDEX magnitude in Niger and Volta river basins
trend reversal from 1981- simulations (Oguntunde et al., 2020); but simulations (Oguntunde et al., 2020); but (Oguntunde et al., 2020); and slight
2010 (Knutson and Zeng, inconsistent changes in CDD in CMIP6 inconsistent changes in CDD in CMIP6 increase in SPI-based meteorological
2018) GCMs (Diedhiou et al., 2018)(Chapter 11 GCMs (Diedhiou et al., 2018)(Chapter 11 drought for overall region (Spinoni et al.,
Supplementary Material (11.SM)), as well as Supplementary Material (11.SM)), as well as 2020). Mixed signal in annual CDD
No evidence that late onset of
2015 wet season in Nigeria in mean precipitation in CMIP6 GCMs in mean precipitation in CMIP6 GCMs (Akinsanola and Zhou, 2018; Dosio et al.,
was due to human (Cook et al., 2020) (Cook et al., 2020) 2019)(Chapter 11 Supplementary Material
contribution (Lawal et al., (11.SM)).
2016)
AGR Medium confidence: Low confidence: Limited Low confidence: Inconsistent signals Low confidence: Inconsistent signals Low confidence: Mixed signal.
ECOL Increased drying based on evidence (geographical and inter-model variations) in (geographical and inter-model variations) in Inconsistent changes depending on
water-balance estimates soil moisture and SPEI-PM (Naumann et al., soil moisture and SPEI-PM (Naumann et al., subregion, indices, and season (Naumann
and SPEI-PM, with
2018; Xu et al., 2019a)(Chapter 11 2018; Xu et al., 2019a; Cook et al., et al., 2018; Cook et al., 2020; Vicente-
stronger signals for SPEI-
PM (Greve et al., 2014; Supplementary Material (11.SM)) 2020)(Chapter 11 Supplementary Material Serrano et al., 2020a)(Chapter 11
Spinoni et al., 2019; (11.SM)) Supplementary Material (11.SM)). Most
Padrón et al., 2020) projections show a drying in Western half
of domain.
HYDR Medium confidence: Low confidence: Low confidence: Limited evidence. One Low confidence: Inconsistent signal Low confidence: Inconsistent
Decrease in streamflow LimitedLimited evidence study shows lack of signal (Touma et al., (Touma et al., 2015; Cook et al., 2020) projections and/or non-robust changes
(Dai and Zhao, 2017; 2015) (Giuntoli et al., 2015; Touma et al., 2015;
Tramblay et al., 2020).
Cook et al., 2020)
North MET Low confidence: Mixed Low confidence: Limited Low confidence: Inconsistent trends. Low confidence:Inconsistent trends. Medium confidence: Decrease in
Eastern signals. Increasing drought evidence on attribution of Inconsistent and weak signals in SPI Inconsistent changes in CDD (Chapter 11 meteorological drought (Sillmann et al.,
Africa in part of the region, in long-term trends. (Nguvava et al., 2019; Xu et al., 2019a), Supplementary Material (11.SM)) and SPI 2013b; Dosio et al., 2019; Cook et al.,
(NEAF) particular in recent two
with high spatial variation (Nguvava et al., (Nguvava et al., 2019; Xu et al., 2019a); but 2020; Spinoni et al., 2020)
decades; but decreasing Robust evidence that recent
drought trends in other part 2019); inconsistent signals in CDD in tendency towards increase in mean
meteorological drought
of domain (NOTE: wetting events (in 2016 and 2017) CMIP6 (Chapter 11 Supplementary Material precipitation (Cook et al., 2020).
trend in Horn of Africa in are not attributable to (11.SM)). Sillmann et al. (2013), (2081-2100)/1981-
Spinoni et al. 2019)(Funk anthropogenic climate 2000, rcp8.5, CMIP3-CMIP5
Do Not Cite, Quote or Distribute 11-137 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
et al., 2015a; Nicholson, change (Lott et al., 2013; Nguvava et al. (2019): projections at 1.5°C Nguvava et al. (2019): projections at 2°C Decrease of CDD
2017; Spinoni et al., 2019) Marthews et al., 2015; Uhe et GWL in Cordex AFR data, compared to GWL in Cordex AFR data, compared to
“No trends in observations al., 2017; Funk et al., 2018b; 1971-2000: non significant changes in SPI- 1971-2000: non significant changes in SPI- Dosio et al. (2019), (2070-2099/1981-
in Ethiopia”; “large Otto et al., 2018a; Philip et 12-based meteorological drought frequency 12-based meteorological drought frequency
2010), rcp 8.5, 23 RCM: Decrease in CDD
variability” (Philip et al., al., 2018b; Kew et al., 2021) and intensity. and intensity.
2018b)
AGR Low confidence: Low confidence: Limited Low confidence: Inconsistent trends Low confidence: Inconsistent trends, but Medium confidence: Decrease in soil
ECOL Inconsistent trends evidence because of lack of (Naumann et al., 2018; Xu et al., tendency to wetting (Naumann et al., 2018; moisture-based drought (Cook et al., 2020;
(Greve et al., 2014; Dai studies 2019a)(Chapter 11 Supplementary Material Xu et al., 2019a; Cook et al., 2020)(Chapter Vicente-Serrano et al., 2020a)(Chapter 11
and Zhao, 2017; Spinoni (11.SM)) 11 Supplementary Material (11.SM)) Supplementary Material (11.SM))
et al., 2019; Padrón et
al., 2020)
HYDR Low confidence: Limited Low confidence: Limited Low confidence: Limited evidence. One Low confidence: Limited evidence due to Medium confidence: Decrease in
evidence evidence on attribution of study shows lack of signal (Touma et al., lack of studies;inconsistent trends (Touma et hydrological drought compared to pre-
long-term trends (Fenta et 2015) al., 2015; Cook et al., 2020) industrial conditions and recent past
al., 2017)
(Giuntoli et al., 2015; Cook et al., 2020)
but some inconsistent signals (Touma et
al., 2015)
Central MET Medium confidence Low confidence: Low confidence; Mixed signal. Drying Low confidence; Mixed signal. Robust Low confidence: Mixed signal,
Africa Decrease in SPI (Spinoni Inconsistent signal in tendency (increasing CDD) in CORDEX drying tendency (increasing CDD) in depending on multi-model experiment and
(CAF) et al., 2019) and mean observations vs models for AFR simulations compared to 1971-2000 CORDEX AFR simulations compared to considered index (Fotso-Nguemo et al.,
rainfall. (Aguilar et al., 1951-2010 trends (Knutson
(Mba et al., 2018); but tendency towards less 1971-2000 (Mba et al., 2018); but 2018; Dosio et al., 2019; Sonkoué et
2009; Hua et al., 2016; Dai and Zeng, 2018); no signal in
and Zhao, 2017) single-model based study drying (CDD decrease) in CMIP6 GCMs inconsistent signal in CMIP6 GCMs (with al., 2019; Spinoni et al., 2020)(Chapter
(Otto et al., 2013) (Chapter 11 Supplementary Material tendency towards CDD decrease (Chapter 11 11 Supplementary Material (11.SM)).
(11.SM)), consistent with increase in Supplementary Material (11.SM));
precipitation at higher warming levels (Cook consistent with projected increase in mean Increase in mean precipitation in CMIP6
et al., 2020). Inconsistent signals in SPI in precipitation (Cook et al., 2020)); GCMs (Cook et al., 2020). Increase in
CMIP5 GCMs (Xu et al., 2019a) inconsistent signals in CDD in CMIP5 CDD (increase in meteorological drought)
GCMs (Sonkoué et al., 2019). Decrease in CORDEX AFR simulations (Dosio et
frequency of SPI-based droughts in CMIP5 al., 2019; Fotso-Nguemo et al., 2019) but
(Xu et al., 2019a). inconsistent CDD signals in CMIP6 (with
tendency towards CDD decrease; Chapter
11 Supplementary Material (11.SM)) and
CMIP5 GCMs (Sonkoué et al., 2019).
Increase in SPI (less drying) in CMIP5
GCMs (Spinoni et al., 2020).
AGR Medium confidence Low confidence: Limited Low confidence: Inconsistent signals. Low confidence: Inconsistent signals. Low confidence. Inconsistent signals.
ECOL Decrease in water-balance evidence due to lack of Slight tendency towards soil moisture Inconsistent trends in duration vs Tendency towards wetting in CMIP6 soil
availability or SPEI, but studies wetting in CMIP5 (Xu et al., 2019a) and frequency of soil moisture-based moisture (Cook et al., 2020)(Chapter 11
some regional variability
CMIP6 (Chapter 11 Supplementary Material drought events in CMIP5 (Xu et al., 2019a); Supplementary Material (11.SM));
and index dependency of
trends (Greve et al., 2014; slight mean soil moisture wetting in CMIP6
Do Not Cite, Quote or Distribute 11-138 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
Dai and Zhao, 2017; (11.SM)); and slight increase (less drying in (Chapter 11 Supplementary Material inconsistent signals in SPEI-PM (Vicente-
Spinoni et al., 2019; SPEI-PM (Naumann et al., 2018) (11.SM)); slight wetting of SPEI-PM based Serrano et al., 2020a)
Padrón et al., 2020) events (Naumann et al., 2018).
HYDR Low confidence: Limited Low confidence: Limited Low confidence: Limited evidence. One Low confidence: Limited evidence and Low confidence: Inconsistent projections
evidence. Decrease in evidence study shows lack of signal (Touma et al., inconsistent trends in mean runoff in two and/or non-robust changes (Giuntoli et al.,
streamflow from 1950- 2015) studies (Touma et al., 2015; Cook et al., 2015; Touma et al., 2015; Cook et al.,
2012 in southern part of
2020) 2020)
domain (Dai and Zhao,
2017)
South MET Low confidence: Low confidence: Limited Low confidence: Inconsistent changes Low confidence: Inconsistent changes Low confidence: Inconsistent trends
Eastern Inconsistent trends in evidence on attribution of (Osima et al., 2018; Xu et al., (Osima et al., 2018; Xu et al., between studies and subregions (Sillmann
Africa SPI (Spinoni et al., 2019) long-term trends. 2019a)(Chapter 11 Supplementary Material 2019a)(Chapter 11 Supplementary Material et al., 2013b; Dosio et al., 2019; Vicente-
(SEAF) but occurrence of strong
(11.SM)) and lack of signal (Nangombe et (11.SM)) and lack of signal (Nangombe et Serrano et al., 2020a)(Chapter 11
drought events in recent Robust evidence that recent
years (Funk et al., 2015a; al., 2018) al., 2018) Supplementary Material (11.SM)).
drought events are not
Nicholson, 2017) attributable to anthropogenic
climate change (Uhe et al., Xu et al. (2019): Inconsistent or weak trends Xu et al. (2019): Inconsistent or weak trends Inconsistent or no changes in SPI
2017; Funk et al., 2018b) in SPI in SPI (Vicente-Serrano et al., 2020a)
Osima et al. (2018): Cordex AFR data,CTL Osima et al. (2018): Cordex AFR data,CTL Sillmann et al. (2013), (2081-2100)/1981-
1971-2000, RCP8.5, 1971-2000, RCP8.5, Robust increase of 2000, rcp8.5, CMIP3-CMIP5:
consistent increase of CDD over southern CDD over southern part. Decrease of CDD
part
Chapter 11 Supplementary Material Dosio et al. (2019), (2070-2099/1981-
Chapter 11 Supplementary Material (11.SM): inconsistent changes in CDD 2010), rcp 8.5, 23 RCM: Decrease in CDD
(11.SM): Inconsistent changes in CDD
Inconsistent trends in CDD in CMIP6
(Chapter 11 Supplementary Material
(11.SM))
AGR Low confidence: Low confidence: Limited Low confidence: Inconsistent trends (Xu et Low confidence; Inconsistent trends (Xu et Low confidence: Inconsistent trends
ECOL Inconsistent trends (Greve evidence due to lack of al., 2019a)(Chapter 11 Supplementary al., 2019a; Cook et al., 2020) (Chapter 11 (Cook et al., 2020; Vicente-Serrano et al.,
et al., 2014; Spinoni et al., studies Material (11.SM)) Supplementary Material (11.SM)) 2020a)(Chapter 11 Supplementary
2019; Padrón et al., 2020)
Material (11.SM))
HYDR Low confidence: Low confidence: Limited Low confidence; Limited evidence. One Low confidence: Limited evidence; Low confidence: Inconsistent trends.
Inconsistent trends (Dai evidence study shows lack of signal (Touma et al., inconsistent trends in runoff in two studies Increase in runoff in a study based on
and Zhao, 2017) 2015) (Touma et al., 2015; Cook et al., 2020) CMIP6 (Cook et al., 2020) but
inconsistent or non-robust trends in studies
based on ISIMIP and CMIP5 ensembles
(Giuntoli et al., 2015; Touma et al., 2015)
Do Not Cite, Quote or Distribute 11-139 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
Western MET Low confidence: Low confidence: Medium confidence: Increase. Increases in High confidence: Increases in dryness Likely: Increase (CDD amd SPI)
Southern Inconsistent trends Limited evidence and dryness (CDD) (Maúre et al., 2018)(Chapter (CDD, DF, NDD) (Maúre et al., 2018; (Sillmann et al., 2013b; Giorgi et al.,
Africa (Spinoni et al., 2019; Dunn inconsistent observed 11 Supplementary Material (11.SM)) both Coppola et al., 2021b) (Chapter 11 2014; Touma et al., 2015; Pinto et al.,
(WSAF) et al., 2020) trends. Supplementary Material (11.SM)); slight but
compared to pre-industrial climate and 2016; Abiodun et al., 2019; Dosio et al.,
weaker increase in SPI (Abiodun et al.,
Dunn et al. (2020): But recent meteorological recent past. Increase in CDD for changes of 2019; Naik and Abiodun, 2019; Xu et al., 2019; Naik and Abiodun, 2020; Spinoni et
Conflicting trends in CDD drought attributable to +0.5°C in global warming based on CMIP5 2019) al., 2020; Coppola et al., 2021b)
depending on time frame anthropogenic climate for overall SREX/AR5 South Africa region
change (Bellprat et al., 2015) (Wartenburger et al., 2017), but only weak Maúre et al. (2018): 25 Cordex AFR Using Cordex , CTL :1981-2010,RCP 8.5
shift in mean precipitation in large-ensemble run ,CTL 1971-2000, RCP8.5, 2071-2100 (Spinoni et al., 2020)
Recent meteorological single-model experiment for +0.5°C of -Increase of CDD Robust increase of drought frequency and
drought (2015/2016 drought severity (SPI-12 )
global warming (Nangombe et al., 2018).
in southern Africa) Coppola et al. (2021b), (2041-2060)/1995-
attributable to anthropogenic Slight but weaker increase in SPI compared 2014, rcp 8.5, CMIP5-CORDEX-CMIP6
to CDD (Abiodun et al., 2019; Xu et al., Based on Giorgi et al., 2014,
climate change (Otto et al., Increase in DF (drought frequency) and
5GCM/1RCM, CTL: 1976-2005, rcp 8.5,
2018b; Funk et al., 2018a; 2019a; Naik and Abiodun, 2020) NDD (number of dry days )
2071-2100:
Yuan et al., 2018; Pascale et
Increase of CDD
al., 2020) Maúre et al. (2018): 25 Cordex AFR run, NB: Weaker signals in SPI (Xu et al., 2019a)
CTL 1971-2000, RCP8.5, Sillmann et al. (2013), (2081-2100)/1981-
Cordex AFR data, CTL 1971-2000, RCP8.5,
-Increase of CDD 2000, rcp8.5, CMIP3-CMIP5
pre-industrial reference period (1861-1890) )
(Abiodun et al., 2019; Naik and Abiodun, Increase of CDD
NB: Weaker signals in SPI (Xu et al., 2019a) 2020): Non-significant increase in SPI-based
drought frequency and intensity. Coppola et al. (2021b), (2080-2099)/1995-
Cordex AFR data, CTL 1971-2000, RCP8.5, 2014, rcp 8.5, CMIP5-CORDEX-CMIP6
pre-industrial reference period (1861-1890)
(Abiodun et al., 2019; Naik and Abiodun, Increase in DF (drought frequency) and
2020) NDD (number of dry days )
Non-significant increase in SPI-based
drought frequency and intensity
Dosio et al. (2019) (2070-2099/1981-
2010), rcp 8.5, 23 RCM: Increase in CDD
Pinto et al. (2016): (2069-2098/1976-
2005), rcp 8.5,4 GCM/2RCM: Increase in
CDD.
AGR Medium confidence: Low confidence: Limited Medium confidence; Drought increase. High confidence: Drought increase. Likely: Drought increase.
ECOL Drought increase based evidence: Decrease in SM both compared to recent Decrease in SM (Xu et al., 2019) (Chapter Decrease in SM (Chapter 11
on water-balance estimates Given small number of past (Xu et al., 2019) and pre-industrial 11 Supplementary Material (11.SM)) Supplementary Material (11.SM))
and SPEI studies based on soil
(Chapter 11 Supplementary Material (Cook et al., 2020); but conflicting changes (Cook et al., 2020) and SPEI-PM
(Greve et al., 2014; moisture (Yuan et al., 2018a)
(11.SM)) baselines; butut conflicting of drought magnitude based on SPEI-PM (Vicente-Serrano et al., 2020a)
Spinoni et al., 2019; and atmospheric drought
changes of drought magnitude based on (Naumann et al., 2018)
Padrón et al., 2020) indices (Nangombe et al.,
2020) SPEI-PM compared to 0.6°C baseline
(Naumann et al., 2018)
Do Not Cite, Quote or Distribute 11-140 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
HYDR Low confidence: Limited Low confidence: Limited Low confidence: Limited evidence. One Medium confidence; Increased drying Medium confidence: Increased drying
evidence. Decrease in evidence study shows lack of signal (Touma et al., (Touma et al., 2015; Cook et al., 2020; (Giuntoli et al., 2015; Touma et al., 2015;
runoff in larger AR5 2015) Zhai et al., 2020a) Cook et al., 2020)
“Southern Africa” region,
but weaker signal
depending on time frame
(Gudmundsson et al., 2019,
2021); non significant
drying tendency (Dai and
Zhao, 2017)
Eastern MET Medium confidence: Low confidence: Limited Medium confidence: Increases in High confidence: Increase in Likely: Increase in meteorological
Southern Dominant increase in evidence on attribution of meteorological drought based on CDD meteorological drought based on drought (CDD amd SPI) (Sillmann et al.,
Africa meteorological drought in long-term trends. (Maúre et al., 2018)(Chapter 11 (CDD,DF,NDD) (Maúre et al., 2018; 2013b; Giorgi et al., 2014; Touma et al.,
(ESAF) SPI and CDD (Spinoni et
Supplementary Material (11.SM)) both Coppola et al., 2021b)(Chapter 11 2015; Pinto et al., 2016; Dosio et al., 2019;
al., 2019; Dunn et al., Medium confidence that
2020) human-influence has compared to pre-industrial climate and Supplementary Material (11.SM)) and SPI Spinoni et al., 2020; Coppola et al.,
contributed to stronger recent past. Non-significant increase in SPI- (Abiodun et al., 2019; Xu et al., 2019a) both 2021b)(Chapter 11 Supplementary
recent meteorological based drought (Abiodun et al., 2017); lack of compared to recent past and pre-industrial Material (11.SM))
drought.(Bellprat et al., 2015; signal in SPI compared to recent past (1970- period. Using Cordex, CTL :1981-2010,RCP 8.5,
Funk et al., 2018a; Yuan et 2000) (Xu et al., 2019a). Increase in CDD 2071-2100 (Spinoni et al., 2020)
al., 2018a) for changes of +0.5°C in global warming Maúre et al. (2018): 25 Cordex AFR run Robust increase of drought frequency and
based on CMIP5 for overall SREX/AR5 ,CTL 1971-2000, RCP8.5: Increase of CDD severity (SPI-12,SPEI-12)
South Africa region (Wartenburger et al.,
2017), but only weak shift in mean (Coppola et al., 2021b), (2041-2060)/1995- Based on Giorgi et al. (2014),
precipitation in large-ensemble single-model 2014, rcp 8.5, CMIP5-CORDEX-CMIP6: 5GCM/1RCM, CTL: 1976-2005, rcp 8.5,
experiment for +0.5°C of global warming Increase in DF (drought frequency) and 2071-2100:
Increase of CDD
(Nangombe et al., 2018). NDD (number of dry days )
Sillmann et al. (2013), (2081-2100)/1981-
Maúre et al. (2018): 25 Cordex AFR run Abiodun et al. (2019): Cordex AFR
data,CTL 1971-2000, RCP8.5, pre-industrial 2000, rcp8.5, CMIP3-CMIP5
,CTL 1971-2000, RCP8.5,
reference period (1861-1890): increase in Increase of CDD
-Increase of CDD
SPI-based meteorological drought frequency
and intensity. (Coppola et al., 2021b), (2080-
Cordex AFR data,CTL 1971-2000, RCP8.5,
2099)/1995-2014, rcp 8.5, CMIP5-
pre-industrial reference period (1861-1890) Xu et al. (2019): Drying in SPI at 2°C
CORDEX-CMIP6
(Abiodun et al., 2019)
compared to 1970-2000 conditions. Increase in DF (drought frequency) and
SPI non-significant drought frequency &
NDD (number of dry days )
intensity increase
Dosio et al. (2019), (2070-2099/1981-
2010), rcp 8.5, 23 RCM
Increase in CDD
Pinto et al. (2016): (2069-2098/1976-
2005), rcp 8.5,4 GCM/2RCM: Increase in
CDD
Do Not Cite, Quote or Distribute 11-141 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
AGR Medium confidence Low confidence: Limited Medium confidence: Increase in drought. Medium confidence: Increase in High confidence: Increase in drought:
ECOL Increase, based on water- evidence Decrease in SM both compared to recent drought;decrease in SM (Xu et al., 2019a; decrease in SM (Chapter 11
balances estimates, PDSI (Yuan et al., 2018a) past (Xu et al., 2019) and pre-industrial Cook et al., 2020) (Chapter 11 Supplementary Material (11.SM))
and SPEI-PM (Greve et al.,
(Chapter 11 Supplementary Material Supplementary Material (11.SM)); but (Cook et al., 2020) and SPEI-PM
2014; Dai and Zhao, 2017;
Spinoni et al., 2019; (11.SM)) baselines; but inconsistent changes inconsistent changes of in drought (Vicente-Serrano et al., 2020a)
Padrón et al., 2020) of drought magnitude based on SPEI-PM magnitude based on SPEI-PM (Naumann et
compared to +0.6°C baseline (Naumann et al., 2018)
al., 2018)
HYDR Low confidence: Limited Low confidence: Limited Low confidence: Limited evidence. One Medium confidence; Increased drying Medium confidence: Increased drying
evidence. evidence study shows lack of signal (Touma et al., (Touma et al., 2015; Cook et al., 2020; (Giuntoli et al., 2015; Touma et al., 2015;
Decrease in runoff in larger 2015) Zhai et al., 2020a). Cook et al., 2020)
AR5 “Southern Africa”
region, but weaker signal
depending on time frame
(Gudmundsson et al., 2019,
2021); non significant
drying tendency (Dai and
Zhao, 2017)
Mada- MET Low confidence: Low confidence: Limited Medium confidence: Increase in High confidence: Increase in Likely: Increase in meteorological
gascar Inconsistent trends evidence meteorological drought based on SPI meteoroloigcal drought based on several drought based on CDD and SPI
(MDG) (Vincent et al., 2011; compared to recent past (Abiodun et al., metrics, including SPI (Abiodun et al., 2019; (Sillmann et al., 2013b; Giorgi et al.,
Spinoni et al., 2019)
2019; Xu et al., 2019a) and CDD compared Xu et al., 2019a), CDD (Chapter 11 2014; Touma et al., 2015; Pinto et al.,
to pre-industrial baseline (Chapter 11 Supplementary Material (11.SM)), and DF 2016; Dosio et al., 2019; Spinoni et al.,
Supplementary Material (11.SM)). (drought frequency) and NDD (number of 2020; Coppola et al., 2021b)
dry days) (Coppola et al., 2021b)
Sillmann et al. (2013), (2081-2100)/1981-
(Coppola et al., 2021b), (2041-2060)/1995- 2000, rcp8.5, CMIP3-CMIP5
2014, rcp 8.5, CMIP5-CORDEX-CMIP6 Increase of CDD
Abiodun et al. (2019): Cordex AFR Increase in DF (drought frequency) and
data,CTL 1971-2000, RCP8.5, pre-industrial NDD (number of dry days ) Spinoni et al. (2020): Using CORDEX,
reference period (1861-1890) CTL:1981-2010,RCP 8.5, 2071-2100
SPI (drought frequency & intensity increase) Abiodun et al. (2019): Cordex AFR Robust increase of drought frequency and
data,CTL 1971-2000, RCP8.5, pre-industrial severity (SPI-12)
reference period (1861-1890): Increase in
SPI-based drought frequency and intensity. (Coppola et al., 2021b), (2080-
2099)/1995-2014, rcp 8.5, CMIP5-
CORDEX-CMIP6
Increase in DF (drought frequency) and
NDD (number of dry days )
Dosio et al. (2019), (2070-2099/1981-
2010), rcp 8.5, 23 RCM: Increase in CDD
Do Not Cite, Quote or Distribute 11-142 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
AGR Low confidence: Low confidence: Limited Low confidence: Inconsistent or weak Medium confidence: Increase in drought. High confidence: Increase in drought.
ECOL Inconsistent trends based evidence trends (Xu et al., 2019) (Chapter 11 Decrease in SM (Chapter 11 Supplementary Robust decrease in SM (Chapter 11
on water-balance Supplementary Material (11.SM)) Material (11.SM); (Cook et al., 2020) and in Supplementary Material (11.SM))
estimates, PDSI and SPEI
(Naumann et al., 2018) SPEI-PM (Naumann et al., 2018) (Cook et al., 2020) and SPEI-PM
(Greve et al., 2014; Dai
and Zhao, 2017; Spinoni et (Vicente-Serrano et al., 2020a)
al., 2019; Padrón et al.,
2020)
HYDR Low confidence: Limited Low confidence: Limited Low confidence: Limited evidence. One Low confidence: Inconsistent trends. Medium confidence: Increase in
evidence. Inconsistent evidence study shows lack of signal (Touma et al., Inconsistent trends (Cook et al., 2020) or drought based on two studies based on
trends in one study (Dai 2015) weak drying (Touma et al., 2015; Zhai et CMIP5 (Giuntoli et al., 2015; Touma et
and Zhao, 2017) al., 2020b)
al., 2015), but some inconsistent trends
in CMIP6 mean runoff trends (Cook et
al., 2020)
1
2 [END TABLE 11.6 HERE]
3
4
5 [START TABLE 11.7 HERE]
6
7 Table 11.7: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for temperature extremes in Asia, subdivided
8 by AR6 regions. See Sections 11.9.1 and 11.9.2 for details
Detection and attribution; Projections
Region Observed trends
event attribution 1.5 °C 2 °C 4 °C
All Asia Most subregions show a very Robust evidence of a human CMIP6 models project a CMIP6 models project a CMIP6 models project a
likely increase in the intensity contribution to the observed robust increase in the robust increase in the robust increase in the
and frequency of hot increase in the intensity and intensity and frequency of intensity and frequency of intensity and frequency of
extremes and decrease in the frequency of hot extremes TXx events and a robust TXx events and a robust TXx events and a robust
intensity and frequency of and decrease in the intensity decrease in the intensity and decrease in the intensity and decrease in the intensity and
cold extremes and frequency of cold frequency of TNn events (Li frequency of TNn events (Li frequency of TNn events (Li
extremes (Hu et al., 2020; et al., 2020). Median increase et al., 2020). Median increase et al., 2020). Median increase
Seong et al., 2020) of more than 0.5°C in the 50- of more than 1°C in the 50- of more than 4.5°C in the 50-
year TXx and TNn events year TXx and TNn events year TXx and TNn events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020) level (Li et al., 2020) level (Li et al., 2020)
Very likely increase in the Human influence very likely Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contributed to the observed frequency of hot extremes : frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Very likely (compared with Extremely likely (compared Virtually certain (compared
intensity and frequency of frequency of hot extremes the recent past (1995-2014)) with the recent past (1995- with the recent past (1995-
cold extremes and decrease in the intensity Extremely likely (compared 2014)) 2014))
and frequency of cold with pre-industrial) Virtually certain (compared Virtually certain (compared
with pre-industrial) with pre-industrial)
extremes
Decrease in the intensity and
frequency of cold extremes: Decrease in the intensity and Decrease in the intensity and
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Final Government Distribution Chapter11 IPCC AR6 WGI
Very likely (compared with frequency of cold extremes: frequency of cold extremes:
the recent past (1995-2014)) Extremely likely (compared Virtually certain (compared
Extremely likely (compared with the recent past (1995- with the recent past (1995-
with pre-industrial) 2014)) 2014))
Virtually certain (compared Virtually certain (compared
with pre-industrial) with pre-industrial)
Russian Arctic (RAR) Significant increases in the Evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Donat et al., 2016a; Sui et and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
al., 2017; Dunn et al., 2020) extremes (Wang et al., 2017c) increase of more than 0.5°C in Median increase of more than Median increase of more than
the 50-year TXx and TNn events 1.5°C in the 50-year TXx and 4.5°C in the 50-year TXx and
compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1.5°C in annual TXx and 2020) and more than 2.5°C in 2020) and more than 5.5°C in
TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5 and RCM simulations
an increase in the intensity and for an increase in the intensity for an increase in the intensity
frequency of hot extremes and and frequency of hot extremes and frequency of hot extremes
decrease in the intensity and and decrease in the intensity and decrease in the intensity
frequency of cold extremes (Xu and frequency of cold extremes and frequency of cold extremes
et al. 2017; Han et al. 2018; (Xu et al. 2017; Han et al. 2018; (Xu et al. 2017; Han et al. 2018;
Khlebnikova et al. 2019) Khlebnikova et al. 2019) Khlebnikova et al. 2019)
Very likely increase in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of intensity and frequency of hot past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes extremes and decrease in the Very likely (compared with pre- Extremely likely (compared 2014))
intensity and frequency of industrial) with pre-industrial) Virtually certain (compared
cold extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Arabian Peninsula (ARP) Significant increases in the Strong evidence of changes CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot from observations that are in increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant the direction of model frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and projected changes for the robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
Do Not Cite, Quote or Distribute 11-144 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
frequency of cold extremes future. The magnitude of and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Dunn et al., 2020; Almazroui projected changes increases et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
et al., 2014; Barlow et al., with global warming. increase of more than 0.5°C in Median increase of more than Median increase of more than
2016; Donat et al., 2014; the 50-year TXx and TNn events 1.5°C in the 50-year TXx and 3.5°C in the 50-year TXx and
Nazrul Islam et al., 2015; compared to the 1°C warming TNn events compared to the TNn events compared to the
Rahimi and Hejabi, 2018; level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
Donat et al., 2014; Rahimi et than 2°C in annual TXx and TNn 2020) and more than 2.5°C in 2020) and more than 5.5°C in
al., 2018) compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5 and RCM simulations
an increase in the intensity and for an increase in the intensity for an increase in the intensity
frequency of hot extremes and and frequency of hot extremes and frequency of hot extremes
decrease in the intensity and and decrease in the intensity and decrease in the intensity
frequency of cold extremes and frequency of cold extremes and frequency of cold extremes
(Almazroui, 2019b) (Almazroui, 2019b) (Almazroui, 2019b)
Very likely increase in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of intensity and frequency of hot past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes extremes and decrease in the Very likely (compared with pre- Extremely likely (compared 2014))
intensity and frequency of industrial) with pre-industrial) Virtually certain (compared
cold extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
West Central Asia (WCA) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Hu et al., 2016; Jiang et al., and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
2013; Dunn et al., 2020) extremes (Seong et al., 2020; increase of more than 0.5°C in Median increase of more than Median increase of more than
Wang et al., 2017; Dong et the 50-year TXx and TNn events 1.5°C in the 50-year TXx and 5°C in the 50-year TXx and
al., 2018; Kim et al., 2019) compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 2°C in annual TXx and TNn 2020) and more than 3°C in 2020) and more than 6°C in
compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
Do Not Cite, Quote or Distribute 11-145 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity and increase in the intensity and increase in the intensity and
frequency of hot extremes and frequency of hot extremes and frequency of hot extremes and
decrease in the intensity and decrease in the intensity and decrease in the intensity and
frequency of cold extremes (Han frequency of cold extremes frequency of cold extremes
et al., 2018) (Han et al., 2018) (Han et al., 2018)
Very likely increase in the High confidence in a human Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contribution to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
West Siberia (WSB) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Degefie et al., 2014; and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Salnikov et al., 2015; Donat extremes (Wang et al., 2017; increase of more than 0.5°C in Median increase of more than Median increase of more than
et al., 2016a; Zhang et al., Seong et al., 2020; Dong et the 50-year TXx and TNn events 1°C in the 50-year TXx and 4°C in the 50-year TXx and
2019c, 2019b; Dunn et al., al., 2018) compared to the 1°C warming TNn events compared to the TNn events compared to the
2020) level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 2°C in annual TXx and TNn 2020) and more than 2.5°C in 2020) and more than 5°C in
compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5 and RCM simulations
an increase in the intensity and for an increase in the intensity for an increase in the intensity
frequency of hot extremes and and frequency of hot extremes and frequency of hot extremes
decrease in the intensity and and decrease in the intensity and decrease in the intensity
frequency of cold extremes (Xu and frequency of cold extremes and frequency of cold extremes
et al. 2017; Han et al. 2018; (Xu et al. 2017; Han et al. 2018; (Xu et al. 2017; Han et al. 2018;
Khlebnikova et al. 2019) Khlebnikova et al. 2019) Khlebnikova et al. 2019)
Very likely increase in the High confidence in a human Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contribution to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
Do Not Cite, Quote or Distribute 11-146 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
intensity and frequency of frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
East Siberia (ESB) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Dashkhuu et al., 2015; Donat and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
et al., 2016a; Zhang et al., extremes (Wang et al., 2017; increase of more than 0.5°C in Median increase of more than Median increase of more than
2019c; Dunn et al., 2020) Seong et al., 2020; Dong et the 50-year TXx and TNn events 1.5°C in the 50-year TXx and 4.5°C in the 50-year TXx and
al., 2018) compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 2°C in annual TXx and TNn 2020) and more than 2.5°C in 2020) and more than 5.5°C in
compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5 and RCM simulations
an increase in the intensity and for an increase in the intensity for an increase in the intensity
frequency of hot extremes and and frequency of hot extremes and frequency of hot extremes
decrease in the intensity and and decrease in the intensity and decrease in the intensity
frequency of cold extremes (Xu and frequency of cold extremes and frequency of cold extremes
et al. 2017; Han et al. 2018; (Xu et al. 2017; Han et al. 2018; (Xu et al. 2017; Han et al. 2018;
Khlebnikova et al. 2019) Khlebnikova et al. 2019) Khlebnikova et al. 2019)
Very likely increase in the High confidence in a human Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contribution to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Do Not Cite, Quote or Distribute 11-147 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
Virtually certain (compared
with pre-industrial)
Russian Far East (RFE) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Donat et al., 2016; Dunn et and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
al., 2020; Zhang et al., 2019b) extremes (Seong et al., 2020; increase of more than 0.5°C in Median increase of more than Median increase of more than
Wang et al., 2017; Dong et the 50-year TXx and TNn events 1°C in the 50-year TXx and 4.5°C in the 50-year TXx and
al., 2018) compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1.5°C in annual TXx and 2020) and more than 2.5°C in 2020) and more than 5°C in
TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5 and RCM simulations
an increase in the intensity and for an increase in the intensity for an increase in the intensity
frequency of hot extremes and and frequency of hot extremes and frequency of hot extremes
decrease in the intensity and and decrease in the intensity and decrease in the intensity
frequency of cold extremes (Xu and frequency of cold extremes and frequency of cold extremes
et al. 2017; Han et al. 2018; (Xu et al. 2017; Han et al. 2018; (Xu et al. 2017; Han et al. 2018;
Khlebnikova et al. 2019). Khlebnikova et al. 2019). Khlebnikova et al. 2019).
Very likely increase in the High confidence in a human Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contribution to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
East Asia (EAS) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Lin et al., 2017; Lu et al., and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
2016, 2018; Wang et al., extremes (Seong et al., 2020; increase of more than 0.5°C in Median increase of more than Median increase of more than
Do Not Cite, Quote or Distribute 11-148 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
2013a; Yin et al., 2017; Zhou Wang et al., 2017; Imada et the 50-year TXx and TNn events 1°C in the 50-year TXx and 4°C in the 50-year TXx and
et al., 2016; Dunn et al., al., 2014, 2019; Kim et al., compared to the 1°C warming TNn events compared to the TNn events compared to the
2020) 2018; Lu et al., 2016, 2018; level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
Takahashi et al., 2016; Ye than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 4.5°C in
and Li, 2017; Zhou et al., TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
2016) (Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5 and RCM simulations
an increase in the intensity and for an increase in the intensity for an increase in the intensity
and frequency of hot extremes and frequency of hot extremes
frequency of hot extremes and
and decrease in the intensity and decrease in the intensity
decrease in the intensity and and frequency of cold extremes and frequency of cold extremes
frequency of cold extremes (Guo (Guo et al., 2018; Imada et al., (Guo et al., 2018; Imada et al.,
et al., 2018; Imada et al., 2019; 2019; Li et al., 2018c; Seo et 2019; Li et al., 2018c; Seo et
Li et al., 2018c; Seo et al., 2014; al., 2014; Sui et al., 2018; Wang al., 2014; Sui et al., 2018; Wang
Sui et al., 2018; Wang et al., et al., 2017a, 2017c; Xu et al., et al., 2017a, 2017c; Xu et al.,
2017a, 2017c; Xu et al., 2016a; 2016a; Zhou et al., 2014; Shi et 2016a; Zhou et al., 2014; Shi et
al., 2018; Sun et al., 2019a) al., 2018; Sun et al., 2019a)
Zhou et al., 2014; Shi et al.,
2018; Sun et al., 2019a)
Very likely increase in the Human influence likely Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contributed to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
East Central Asia (ECA) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Dunn et al., 2020) and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
extremes (Seong et al., 2020; increase of more than 0.5°C in Median increase of more than Median increase of more than
Wang et al., 2017; Dong et the 50-year TXx and TNn events 1°C in the 50-year TXx and 3.5°C in the 50-year TXx and
al., 2018; Kim et al., 2019) compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 2°C in annual TXx and TNn 2020) and more than 2.5°C in 2020) and more than 5.5°C in
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Final Government Distribution Chapter11 IPCC AR6 WGI
compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity and increase in the intensity and increase in the intensity and
frequency of hot extremes and frequency of hot extremes and frequency of hot extremes and
decrease in the intensity and decrease in the intensity and decrease in the intensity and
frequency of cold extremes (Han frequency of cold extremes frequency of cold extremes
et al., 2018) (Han et al., 2018) (Han et al., 2018)
Very likely increase in the High confidence in a human Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contribution to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Tibetan Plateau (TIB) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Donat et al., 2016a; Hu et al., and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
2016; Sun et al., 2017; Yin et extremes (Seong et al., 2020; increase of more than 0.5°C in Median increase of more than Median increase of more than
al., 2019; Zhang et al., 2019c; Wang et al., 2017; Yin et al., the 50-year TXx and TNn events 1°C in the 50-year TXx and 4°C in the 50-year TXx and
Dunn et al., 2020) 2019) compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 4.5°C in
TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5 and RCM simulations
an increase in the intensity and for an increase in the intensity for an increase in the intensity
frequency of hot extremes and and frequency of hot extremes and frequency of hot extremes
decrease in the intensity and and decrease in the intensity and decrease in the intensity
frequency of cold extremes and frequency of cold extremes and frequency of cold extremes
(Zhou et al., 2014; Singh and (Zhou et al., 2014; Singh and (Zhou et al., 2014; Singh and
Goyal, 2016; Zhang et al., Goyal, 2016; Zhang et al., Goyal, 2016; Zhang et al.,
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Final Government Distribution Chapter11 IPCC AR6 WGI
2016a; Xu et al., 2017; Han et 2016a; Xu et al., 2017; Han et 2016a; Xu et al., 2017; Han et
al., 2018; Li et al., 2018a) al., 2018; Li et al., 2018a) al., 2018; Li et al., 2018a)
Very likely increase in the High confidence in a human Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contribution to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
South Asia (SAS) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Chakraborty et al., 2018; and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Dimri, 2019; Donat et al., extremes (Seong et al., 2020; increase of more than 0C in the Median increase of more than Median increase of more than
2016; Dunn et al., 2020; Roy, Wang et al., 2017; Wehner et 50-year TXx and TNn events 1°C in the 50-year TXx and 3.5°C in the 50-year TXx and
2019; Sheikh et al., 2015; al., 2016; Kumar, 2017; van compared to the 1°C warming TNn events compared to the TNn events compared to the
Rohini et al., 2016; Zahid and Oldenborgh et al., 2018) level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
Rasul, 2012) than 1°C in annual TXx and TNn 2020) and more than 1.5°C in 2020) and more than 4°C in
compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5 and RCM simulations
an increase in the intensity and for an increase in the intensity for an increase in the intensity
frequency of hot extremes and and frequency of hot extremes and frequency of hot extremes
decrease in the intensity and and decrease in the intensity and decrease in the intensity
frequency of cold extremes (Ali and frequency of cold extremes and frequency of cold extremes
et al., 2019; Han et al., 2018; (Ali et al., 2019; Han et al., (Ali et al., 2019; Han et al.,
Kharin et al., 2018; Sillmann et 2018; Kharin et al., 2018; 2018; Kharin et al., 2018;
al., 2013; Xu et al., 2017; Murari Sillmann et al., 2013; Xu et al., Sillmann et al., 2013; Xu et al.,
et al., 2015; Nasim et al., 2018) 2017; Murari et al., 2015; 2017; Murari et al., 2015;
Nasim et al., 2018) Nasim et al., 2018)
High confidence in the High confidence in a human Increase in the intensity and Increase in the intensity and Increase in the intensity and
increase in the intensity and contribution to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
frequency of hot extremes increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
and decrease in the intensity frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
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Final Government Distribution Chapter11 IPCC AR6 WGI
and frequency of cold and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Southeast Asia (SEA) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Donat et al., 2016a; Supari et and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
al., 2017; Cheong et al., 2018; extremes (Seong et al., 2020; increase of more than 0C in the Median increase of more than Median increase of more than
Zhang et al., 2019c; Dunn et Wang et al., 2017; King et al., 50-year TXx and TNn events 0.5°C in the 50-year TXx and 2.5°C in the 50-year TXx and
al., 2020) 2016; Min et al., 2020) compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1°C in annual TXx and TNn 2020) and more than 1.5°C in 2020) and more than 4°C in
compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex)
Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity and increase in the intensity and increase in the intensity and
frequency of hot extremes and frequency of hot extremes and
frequency of hot extremes and
decrease in the intensity and decrease in the intensity and
decrease in the intensity and frequency of cold extremes frequency of cold extremes
frequency of cold extremes (Han (Kharin et al., 2018; Xu et al., (Kharin et al., 2018; Xu et al.,
et al., 2018; Kharin et al., 2018; 2017). 2017)..
Xu et al., 2017)
High confidence in the High confidence in a human Increase in the intensity and Increase in the intensity and Increase in the intensity and
increase in the intensity and contribution to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
frequency of hot extremes increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
and decrease in the intensity frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
and frequency of cold and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
extremes and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial). with pre-industrial) 2014))
Virtually certain (compared
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Final Government Distribution Chapter11 IPCC AR6 WGI
with pre-industrial)
1 [END TABLE 11.7 HERE]
2
3
4 [START TABLE 11.8 HERE]
5
6 Table 11.8: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for heavy precipitation in Asia, subdivided
7 by AR6 regions. See Sections 11.9.1 and 11.9.3 for details
Detection and attribution;
Region Observed trends Projections
event attribution
1.5 °C 2 °C 4 °C
All Asia Significant intensification of Robust evidence of a human CMIP6 models project a CMIP6 models project a CMIP6 models project a
heavy precipitation (Sun et contribution to the observed robust increase in the robust increase in the robust increase in the
al., 2020) intensification of heavy intensity and frequency of intensity and frequency of intensity and frequency of
precipitation heavy precipitation (Li et al., heavy precipitation (Li et al., heavy precipitation ((Li et al.,
2020a). Median increase of 2020a). Median increase of 2020a). Median increase of
more than 2% in the 50-year more than 6% in the 50-year more than 15% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) level (Li et al., 2020a) level (Li et al., 2020a)
Likely intensification of heavy Human influence likely Intensification of heavy Intensification of heavy Intensification of heavy
precipitation contributed to the observed precipitation: precipitation: precipitation:
intensification of heavy Likely (compared with the Very likely (compared with Virtually certain (compared
precipitation recent past (1995-2014)) the recent past (1995-2014)) with the recent past (1995-
Very likely (compared with Extremely likely (compared 2014))
pre-industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
Russian Arctic (RAR) Insufficient data and a lack of Limited evidence CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
agreement on the evidence of increase in the intensity and increase in the intensity and increase in the intensity and
trends (Sun et al., 2020; Dunn frequency of heavy frequency of heavy frequency of heavy
et al., 2020) precipitation (Li et al., 2020; precipitation (Li et al., 2020; precipitation (Li et al., 2020;
Annex). Median increase of Annex). Median increase of Annex). Median increase of
more than 4% in the 50-year more than 8% in the 50-year more than 25% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and more level (Li et al., 2020a) and more level (Li et al., 2020a) and more
than 10% in annual Rx1day and than 10% in annual Rx1day, than 25% in annual Rx1day and
Rx5day and 8% in annual Rx5day, and Rx30day Rx5day and 20% in annual
Rx30day compared to pre- compared to pre-industrial Rx30day compared to pre-
industrial (Annex). (Annex). industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity of increase in the intensity of increase in the intensity of
heavy precipitation (Sillmann et heavy precipitation (Sillmann et heavy precipitation (Sillmann et
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Final Government Distribution Chapter11 IPCC AR6 WGI
al., 2013b; Han et al., 2018; al., 2013b; Han et al., 2018; al., 2013b; Han et al., 2018;
Kharin et al., 2018; Kharin et al., 2018; Kharin et al., 2018;
Khlebnikova et al., 2019b) Khlebnikova et al., 2019b) Khlebnikova et al., 2019b)
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Likely (compared with the Very likely (compared with the Virtually certain (compared
recent past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
Very likely (compared with pre- Extremely likely (compared 2014))
industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
Arabian Peninsula (ARP) Insufficient data and a lack of Limited evidence CMIP6 models project an CMIP6 models project a robust
agreement on the evidence of increase in the intensity and increase in the intensity and
trends (Sun et al., 2020; Dunn frequency of heavy frequency of heavy
et al., 2020; Atif et al., 2020; precipitation (Li et al., 2020; precipitation (Li et al., 2020;
Donat et al., 2014; Rahimi Annex). Median increase of Annex). Median increase of
and Fatemi, 2019) more than 8% in the 50-year more than 20% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and more level (Li et al., 2020a) and more
than 15% in annual Rx1day, than 40% in annual Rx1day and
Rx5day, and Rx30day Rx5day and 45% in annual
compared to pre-industrial Rx30day compared to pre-
(Annex). industrial (Annex).
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Low confidence (compared with Medium confidence (compared Likely (compared with the
the recent past (1995-2014)) with the recent past (1995- recent past (1995-2014))
Medium confidence (compared 2014)) Very likely (compared with pre-
with pre-industrial) High confidence (compared industrial)
with pre-industrial)
West Central Asia (WCA) Intensification of heavy Limited evidence CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
precipitation (Sun et al., increase in the intensity and increase in the intensity and increase in the intensity and
2020; Hu et al., 2016; Zhang frequency of heavy frequency of heavy frequency of heavy
et al., 2017). precipitation (Li et al., 2020; precipitation (Li et al., 2020; precipitation (Li et al., 2020;
Annex). Median increase of Annex). Median increase of Annex). Median increase of
more than 2% in the 50-year more than 6% in the 50-year more than 15% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and more level (Li et al., 2020a) and more level (Li et al., 2020a) and more
than 6% in annual Rx1day and than 8% in annual Rx1day and than 20% in annual Rx1day and
Rx5day and 4% in annual Rx5day and 6% in annual Rx5day and 15% in annual
Rx30day compared to pre- Rx30day compared to pre- Rx30day compared to pre-
industrial (Annex). industrial (Annex). industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity of increase in the intensity of increase in the intensity of
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Final Government Distribution Chapter11 IPCC AR6 WGI
heavy precipitation (Han et al., heavy precipitation (Han et al., heavy precipitation (Han et al.,
2018) 2018) 2018)
Medium confidence in the Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
intensitification of heavy precipitation: precipitation: precipitation:
precipitation Likely (compared with the Very likely (compared with the Virtually certain (compared
recent past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
Very likely (compared with pre- Extremely likely (compared 2014))
industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
West Siberia (WSB) Significant intensification of Limited evidence CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
heavy precipitation (Sun et increase in the intensity and increase in the intensity and increase in the intensity and
al., 2020; Zhang et al., 2017) frequency of heavy frequency of heavy frequency of heavy
precipitation (Li et al., 2020; precipitation (Li et al., 2020; precipitation (Li et al., 2020;
Annex). Median increase of Annex). Median increase of Annex). Median increase of
more than 2% in the 50-year more than 4% in the 50-year more than 15% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and more level (Li et al., 2020a) and more level (Li et al., 2020a) and more
than 6% in annual Rx1day, than 8% in annual Rx1day and than 15% in annual Rx1day,
Rx5day, and Rx30day Rx5day and 6% in annual Rx5day, and Rx30day
compared to pre-industrial Rx30day compared to pre- compared to pre-industrial
(Annex). industrial (Annex). (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity of increase in the intensity of increase in the intensity of
heavy precipitation (Sillmann et heavy precipitation (Sillmann et heavy precipitation (Sillmann et
al., 2013b; Xu et al., 2017; Han al., 2013b; Xu et al., 2017; Han al., 2013b; Xu et al., 2017; Han
et al., 2018; Kharin et al., 2018; et al., 2018; Kharin et al., 2018; et al., 2018; Kharin et al., 2018;
Khlebnikova et al., 2019b) Khlebnikova et al., 2019b) Khlebnikova et al., 2019b)
High confidence in the Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
intensitification of heavy precipitation: precipitation: precipitation:
precipitation Likely (compared with the Very likely (compared with the Virtually certain (compared
recent past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
Very likely (compared with pre- Extremely likely (compared 2014))
industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
East Siberia (ESB) Intensification of heavy Limited evidence CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
precipitation (Knutson and increase in the intensity and increase in the intensity and increase in the intensity and
Zeng, 2018; Sun et al., 2020; frequency of heavy frequency of heavy frequency of heavy
Dunn et al., 2020) precipitation (Li et al., 2020; precipitation (Li et al., 2020; precipitation (Li et al., 2020;
Annex). Median increase of Annex). Median increase of Annex). Median increase of
more than 2% in the 50-year more than 4% in the 50-year more than 20% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and more level (Li et al., 2020a) and more level (Li et al., 2020a) and more
than 6% in annual Rx1day and than 8% in annual Rx1day and than 20% in annual Rx1day and
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Final Government Distribution Chapter11 IPCC AR6 WGI
Rx5day and 4% in annual Rx5day and 6% in annual Rx5day and 15% in annual
Rx30day compared to pre- Rx30day compared to pre- Rx30day compared to pre-
industrial (Annex). industrial (Annex). industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity of increase in the intensity of increase in the intensity of
heavy precipitation (Sillmann et heavy precipitation (Sillmann et heavy precipitation (Sillmann et
al., 2013b; Xu et al., 2017; Han al., 2013b; Xu et al., 2017; Han al., 2013b; Xu et al., 2017; Han
et al., 2018; Kharin et al., 2018; et al., 2018; Kharin et al., 2018; et al., 2018; Kharin et al., 2018;
Khlebnikova et al., 2019b) Khlebnikova et al., 2019b) Khlebnikova et al., 2019b)
Medium confidence in the Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
intensitification of heavy precipitation: precipitation: precipitation:
precipitation Likely (compared with the Very likely (compared with the Virtually certain (compared
recent past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
Very likely (compared with pre- Extremely likely (compared 2014))
industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
Russian Far East (RFE) Intensification of heavy Limited evidence CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
precipitation (Sun et al., increase in the intensity and increase in the intensity and increase in the intensity and
2020) frequency of heavy frequency of heavy frequency of heavy
precipitation (Li et al., 2020; precipitation (Li et al., 2020; precipitation (Li et al., 2020;
Annex). Median increase of Annex). Median increase of Annex). Median increase of
more than 4% in the 50-year more than 8% in the 50-year more than 25% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and more level (Li et al., 2020a) and more level (Li et al., 2020a) and more
than 8% in annual Rx1day and than 10% in annual Rx1day, than 25% in annual Rx1day and
Rx5day and 6% in annual Rx5day, and Rx30day Rx5day and 20% in annual
Rx30day compared to pre- compared to pre-industrial Rx30day compared to pre-
industrial (Annex). (Annex). industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity of increase in the intensity of increase in the intensity of
heavy precipitation (Sillmann et heavy precipitation (Sillmann et heavy precipitation (Sillmann et
al., 2013b; Xu et al., 2017; Han al., 2013b; Xu et al., 2017; Han al., 2013b; Xu et al., 2017; Han
et al., 2018; Kharin et al., 2018) et al., 2018; Kharin et al., 2018) et al., 2018; Kharin et al., 2018)
Medium confidence in the Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
intensitification of heavy precipitation: precipitation: precipitation:
precipitation Likely (compared with the Very likely (compared with the Virtually certain (compared
recent past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
Very likely (compared with pre- Extremely likely (compared 2014))
industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
East Asia (EAS) Intensification of heavy Disagreement among studies CMIP6 models project an CMIP6 models project a robust CMIP6 models project a robust
precipitation (Sun et al., (Chen and Sun, 2017; Li et increase in the intensity and increase in the intensity and increase in the intensity and
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Final Government Distribution Chapter11 IPCC AR6 WGI
2020; Dunn et al., 2020; Baek al., 2017; Burke et al., 2016; frequency of heavy frequency of heavy frequency of heavy
et al., 2017; Nayak et al., Zhou et al., 2013; Ma et al., precipitation (Li et al., 2020; precipitation (Li et al., 2020; precipitation (Li et al., 2020;
2017; Ye and Li, 2017; Zhou 2017) Annex). Median increase of Annex). Median increase of Annex). Median increase of
et al., 2016) more than 2% in the 50-year more than 6% in the 50-year more than 20% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and more level (Li et al., 2020a) and more level (Li et al., 2020a) and more
than 4% in annual Rx1day and than 6% in annual Rx1day and than 20% in annual Rx1day and
Rx5day and 0% in annual Rx5day and 2% in annual Rx5day and 10% in annual
Rx30day compared to pre- Rx30day compared to pre- Rx30day compared to pre-
industrial (Annex). industrial (Annex). industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity of increase in the intensity of increase in the intensity of
heavy precipitation (Ahn et al., heavy precipitation (Ahn et al., heavy precipitation (Ahn et al.,
2016; Guo et al., 2018; 2016; Guo et al., 2018; 2016; Guo et al., 2018;
Hatsuzuka et al., 2020; Kawase Hatsuzuka et al., 2020; Kawase Hatsuzuka et al., 2020; Kawase
et al., 2019; Kim et al., 2018; et al., 2019; Kim et al., 2018; et al., 2019; Kim et al., 2018;
Kusunoki, 2018; Kusunoki and Kusunoki, 2018; Kusunoki and Kusunoki, 2018; Kusunoki and
Mizuta, 2013; Li et al., 2018a; Mizuta, 2013; Li et al., 2018a; Mizuta, 2013; Li et al., 2018a;
Nayak and Dairaku, 2016; Ohba Nayak and Dairaku, 2016; Ohba Nayak and Dairaku, 2016; Ohba
and Sugimoto, 2020, 2019; Seo and Sugimoto, 2020, 2019; Seo and Sugimoto, 2020, 2019; Seo
et al., 2014; Wang et al., 2017a, et al., 2014; Wang et al., 2017a, et al., 2014; Wang et al., 2017a,
2017b; Zhou et al., 2014; Li et 2017b; Zhou et al., 2014; Li et 2017b; Zhou et al., 2014; Li et
al., 2018b) al., 2018b) al., 2018b)
Medium confidence in the Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
intensitification of heavy precipitation: precipitation: precipitation:
precipitation High confidence (compared Likely (compared with the Extremely likely (compared
with the recent past (1995- recent past (1995-2014)) with the recent past (1995-
2014)) Very likely (compared with pre- 2014))
Likely (compared with pre- industrial) Virtually certain (compared
industrial) with pre-industrial)
East Central Asia (ECA) Intensification of heavy Limited evidence CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
precipitation (Sun et al., increase in the intensity and increase in the intensity and increase in the intensity and
2020) frequency of heavy frequency of heavy frequency of heavy
precipitation (Li et al., 2020; precipitation (Li et al., 2020; precipitation (Li et al., 2020;
Annex). Median increase of Annex). Median increase of Annex). Median increase of
more than 4% in the 50-year more than 6% in the 50-year more than 20% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and more level (Li et al., 2020a) and more level (Li et al., 2020a) and more
than 8% in annual Rx1day and than 10% in annual Rx1day, than 25% in annual Rx1day,
Rx5day and 6% in annual Rx5day, and Rx30day Rx5day, and Rx30day
Rx30day compared to pre- compared to pre-industrial compared to pre-industrial
industrial (Annex). (Annex). (Annex).
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Final Government Distribution Chapter11 IPCC AR6 WGI
Medium confidence in the Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
intensitification of heavy precipitation: precipitation: precipitation:
precipitation Likely (compared with the Very likely (compared with the Virtually certain (compared
recent past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
Very likely (compared with pre- Extremely likely (compared 2014))
industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
Tibetan Plateau (TIB) Intensification of heavy Limited evidence CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
precipitation (Sun et al., increase in the intensity and increase in the intensity and increase in the intensity and
2020; Jiang et al., 2013; Hu et frequency of heavy frequency of heavy frequency of heavy
al., 2016; Ge et al., 2017; precipitation (Li et al., 2020; precipitation (Li et al., 2020; precipitation (Li et al., 2020;
Zhan et al., 2017; Liu et al., Annex). Median increase of Annex). Median increase of Annex). Median increase of
2019) more than 2% in the 50-year more than 4% in the 50-year more than 20% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and more level (Li et al., 2020a) and more level (Li et al., 2020a) and more
than 4% in annual Rx1day and than 8% in annual Rx1day and than 25% in annual Rx1day and
Rx5day and 2% in annual Rx5day and 6% in annual Rx5day and 20% in annual
Rx30day compared to pre- Rx30day compared to pre- Rx30day compared to pre-
industrial (Annex). industrial (Annex). industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity of increase in the intensity of increase in the intensity of
heavy precipitation (Zhou et al., heavy precipitation (Zhou et al., heavy precipitation (Zhou et al.,
2014; Zhang et al., 2015c; Gao 2014; Zhang et al., 2015c; Gao 2014; Zhang et al., 2015c; Gao
et al., 2018; Han et al., 2018) et al., 2018; Han et al., 2018) et al., 2018; Han et al., 2018)
Medium confidence in the Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
intensitification of heavy precipitation: precipitation: precipitation:
precipitation Likely (compared with the Very likely (compared with the Virtually certain (compared
recent past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
Very likely (compared with pre- Extremely likely (compared 2014))
industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
South Asia (SAS) Significant intensification of Disagreement among studies CMIP6 models project an CMIP6 models project an CMIP6 models project a robust
heavy precipitation (Kim et (Mukherjee et al., 2018a) increase in the intensity and increase in the intensity and increase in the intensity and
al., 2019; Malik et al., 2016; (Singh et al., 2014a; van frequency of heavy frequency of heavy frequency of heavy
Pai et al., 2015; Rohini et al., Oldenborgh et al., 2016) precipitation (Li et al., 2020; precipitation (Li et al., 2020; precipitation (Li et al., 2020;
2016; Roxy et al., 2017; Annex). Median increase of Annex). Median increase of Annex). Median increase of
Sheikh et al., 2015; Singh et more than 2% in the 50-year more than 6% in the 50-year more than 25% in the 50-year
al., 2014; Dunn et al., 2020; Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
Hussain and Lee, 2013; Kim compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
et al., 2019; Malik et al., level (Li et al., 2020a) and more level (Li et al., 2020a) and more level (Li et al., 2020a) and more
2016) than 8% in annual Rx1day and than 10% in annual Rx1day and than 30% in annual Rx1day and
Rx5day and 4% in annual Rx5day and 8% in annual Rx5day and 25% in annual
Rx30day compared to pre- Rx30day compared to pre- Rx30day compared to pre-
industrial (Annex). industrial (Annex). industrial (Annex).
Do Not Cite, Quote or Distribute 11-158 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity of increase in the intensity of increase in the intensity of
heavy precipitation (Sillmann et heavy precipitation (Sillmann et heavy precipitation (Sillmann et
al., 2013b; Xu et al., 2017; Han al., 2013b; Xu et al., 2017; Han al., 2013b; Xu et al., 2017; Han
et al., 2018; Mukherjee et al., et al., 2018; Mukherjee et al., et al., 2018; Mukherjee et al.,
2018a; Ali et al., 2019b; Rai et 2018a; Ali et al., 2019b; Rai et 2018a; Ali et al., 2019b; Rai et
al., 2019) al., 2019) al., 2019)
High confidence in the Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
intensitification of heavy precipitation: precipitation: precipitation:
precipitation Medium confidence (compared High confidence (compared Very likely (compared with the
with the recent past (1995- with the recent past (1995- recent past (1995-2014))
2014)) 2014)) Extremely likely (compared
High confidence (compared Likely (compared with pre- with pre-industrial)
with pre-industrial) industrial)
Southeast Asia (SEA) Intensification of heavy Evidence of a human CMIP6 models project an CMIP6 models project an CMIP6 models project a
precipitation (Sun et al., contribution for some events increase in the intensity and increase in the intensity and robust increase in the
2020; Cheong et al., 2018; Li (Otto et al., 2018a), but frequency of heavy frequency of heavy intensity and frequency of
et al., 2018c; Siswanto et al., cannot be generalized precipitation (Li et al., 2020; precipitation (Li et al., 2020; heavy precipitation (Li et al.,
2015; Supari et al., 2017; Annex). Median increase of Annex). Median increase of 2020; Annex). Median
Villafuerte and Matsumoto, more than 0% in the 50-year more than 4% in the 50-year increase of more than 10% in
2015) Rx1day and Rx5day events Rx1day and Rx5day events the 50-year Rx1day and
compared to the 1°C warming compared to the 1°C warming Rx5day events compared to
level (Li et al., 2020a) and level (Li et al., 2020a) and the 1°C warming level (Li et
more than 4% in annual more than 6% in annual al., 2020a) and more than
Rx1day and Rx5day and 2% Rx1day and Rx5day and 4% 20% in annual Rx1day and
in annual Rx30day compared in annual Rx30day compared Rx5day and 10% in annual
to pre-industrial (Annex). to pre-industrial (Annex). Rx30day compared to pre-
industrial (Annex).
Additional evidence from Additional evidence from
CMIP5 and CORDEX CMIP5 and CORDEX Additional evidence from
simulations for an increase in simulations for an increase in CMIP5 and CORDEX
the intensity of heavy the intensity of heavy simulations for an increase in
precipitation (Xu et al., 2017; precipitation (Xu et al., 2017; the intensity of heavy
Han et al., 2018; Tangang et Han et al., 2018; Tangang et precipitation (Xu et al., 2017;
al., 2018; Trinh-Tuan et al., al., 2018; Trinh-Tuan et al., Han et al., 2018; Tangang et
2019; Basconcillo et al., 2019; Basconcillo et al., al., 2018; Trinh-Tuan et al.,
2016; Ge et al., 2017; Han et 2016; Ge et al., 2017; Han et 2019; Basconcillo et al.,
al., 2018; Marzin et al., 2015; al., 2018; Marzin et al., 2015; 2016; Ge et al., 2017; Han et
Tangang et al., 2018; Trinh- Tangang et al., 2018; Trinh- al., 2018; Marzin et al., 2015;
Tuan et al., 2019; Xu et al., Tuan et al., 2019; Xu et al., Tangang et al., 2018; Trinh-
2017) 2017) Tuan et al., 2019; Xu et al.,
2017)
Medium confidence in the Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
intensitification of heavy precipitation: precipitation: precipitation:
precipitation Medium confidence High confidence (compared Very likely (compared with
(compared with the recent with the recent past (1995- the recent past (1995-2014))
Do Not Cite, Quote or Distribute 11-159 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
past (1995-2014)) 2014)) Extremely likely (compared
High confidence (compared Likely (compared with pre- with pre-industrial)
with pre-industrial) industrial)
1
2 [END TABLE 11.8 HERE]
3
4
5 [START TABLE 11.9 HERE]
6
7 Table 11.9: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for meteorological droughts (MET),
8 agricultural and ecological droughts (AGR/ECOL), and hydrological droughts (HYDR) in Asia, subdivided by AR6 regions. See Sections 11.9.1 and 11.9.4 for details
Region/ Drought Observed trends Human contribution Projections
type +1.5 °C +2 °C +4 °C
Russian MET Low confidence: Low confidence: Low confidence: Low confidence: Limited evidence, Medium confidence: Decrease in
Arctic Limited evidence. Tendency Limited evidence Limited evidence. Slight but some evidence of decrease in dry drought severity based on SPI
(RAR) towards decrease in CDD decrease in CDD in CMIP6 spell duration (Khlebnikova et al., (Touma et al., 2015; Spinoni et al.,
(Dunn et al., 2020). Lack of (Chapter 11 Supplementary 2019b)(Chapter 11 Supplementary 2020) and CDD (Chapter 11
data in (Spinoni et al., 2019). Material (11.SM)) Material (11.SM)) Supplementary Material (11.SM)).
AGR, Low confidence: Low confidence: Low confidence: Inconsistent Low confidence: Inconsistent Low confidence: Inconsistent trends.
ECOL Inconsistent trends (Greve et Limited evidence changes in soil moisture (Xu et changes in soil moisture, variations Inconsistent trends across models and
al., 2014; Padrón et al., 2020). al., 2019a)(Chapter 11 across subregions (Xu et al., 2019a) subregions for surface and total soil
Supplementary Material (Chapter 11 Supplementary Material moisture (Dai et al., 2018; Lu et al.,
(11.SM)). (11.SM)) 2019; Cook et al., 2020)(Chapter 11
Supplementary Material (11.SM));
Slight drying in PDSI (Dai et al.,
2018); inconsistent trends or wetting in
SPEI-PM in CMIP5 (Cook et al.,
2014b; Vicente-Serrano et al., 2020a).
Do Not Cite, Quote or Distribute 11-160 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Inconsistent Low confidence: Mixed signals
evidence. Limited evidence. evidence. One study shows lack changes.. Increasing runoff in CMIP6 among studies (Prudhomme et al.,
of signal (Touma et al., 2015) (Cook et al., 2020) , inconsistent 2014; Giuntoli et al., 2015; Touma et
signal in SRI depending on subregion al., 2015; Cook et al., 2020)
in CMIP5(Touma et al., 2015), or lack
of signal (Zhai et al., 2020b) in
available studies.
(Cook et al., 2020): Increasing runoff
in one study based on CMIP6 GCMs
(Zhai et al., 2020b): Lack of signal in
one study based on single
hydrological model driven by HAPPI-
MIP GCM simulations
Touma et al. (2015): Inconsistent
signal in SRI depending on subregion
(CMIP5 GCMs)
Arabian Low confidence: Low confidence: Low confidence: Limited Low confidence: Limited Low confidence: Limited evidence
Peninsula Inconsistent or no signal Limited evidence evidence and inconsistent evidence and inconsistent trends and inconsistent trends (Touma et
(ARP) (Almazroui, 2019a; Almazroui (Barlow and Hoell, trends (Xu et al., (Xu et al., 2019a)(Chapter 11 al., 2015; Tabari and Willems,
and Islam, 2019). 2015; Barlow et al., 2019a)(Chapter 11 Supplementary Material (11.SM)). 2018)(Chapter 11 Supplementary
2016) Supplementary Material Material (11.SM)).
(Dunn et al., 2020): Wetting (11.SM)).
based on CDD in part of (Touma et al., 2015): Inconsistant
MET
domain, but missing data in projections in CMIP5
large fraction of region. (Tabari and Willems, 2018):
Dominant lack of signal
(Spinoni et al., 2019): Missing
data in this region. Chapter 11 Supplementary Material
(11.SM)): decreasing dryness based
on CDD
AGR, Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Low confidence: Mixed signal
ECOL evidence. Drying in fraction of Limited evidence evidence and inconsistent Limited evidence and inconsistent between different metrics. including
region in one study, but trends (Xu et al., trends (Xu et al., 2019a; Cook et total and surface soil moisture
missing data in rest of region 2019a)(Chapter 11 al., 2020)(Chapter 11 (Chapter 11 Supplementary Material
(Greve et al., 2014). Supplementary Material Supplementary Material (11.SM)) (11.SM))(Rajsekhar and Gorelick,
(11.SM)) 2017; Dai et al., 2018; Lu et al.,
(Greve et al., 2014) : Drying in 2019; Cook et al., 2020), PDSI (Dai
part of region, but missing data (Naumann et al., 2018): et al., 2018) and SPEI-PM (Cook et
in large fraction of region. Missing data al., 2014b; Vicente-Serrano et al.,
2020a).
(Padrón et al., 2020) : Missing
data.
(Spinoni et al., 2019) : Missing
data.
Do Not Cite, Quote or Distribute 11-161 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited evidence Low confidence: Inconsistent
evidence Drying in one study Limited evidence evidence. One study shows and inconsistent trends (Touma et trends between models and studies
in northern part of region but lack of signal (Touma et al., al., 2015; Cook et al., 2020; Zhai et (Prudhomme et al., 2014; Giuntoli et
missing data in rest of region 2015). al., 2020b) al., 2015; Touma et al., 2015; Cook
(Dai and Zhao, 2017) et al., 2020)
West MET Low confidence: Inconsistent Low confidence: Low confidence: t Limited Low confidence: Inconsistent, weak Low confidence: Mixed signals
Central trendsbetween subregions, Limited evidence evidence. Inconsistent or weak and/or non-significant trends in SPI between models and between regions
Asia based both on CDD and SPI trends in available analyses (Xu and CDD (Xu et al., 2019a; Spinoni et (Touma et al., 2015; Han et al., 2018;
(WCA) (Spinoni et al., 2019; Dunn et et al., 2019a)(Chapter 11
al., 2020; Yao et al., 2020)(Chapter 11 Tabari and Willems, 2018; Spinoni et
al., 2020; Sharafati et al., 2020; Supplementary Material (11.SM))
Yao et al., 2020). Supplementary Material (11.SM)). al., 2020; Yao et al., 2020)(Chapter 11
Supplementary Material (11.SM))
AGR, Medium confidence: Increase Low confidence: Low confidence: Mixed Low confidence: Mixed signals in Medium confidence: Increased
ECOL in drought severity. Limited evidence. signals in changes in drought changes in drought severity, drying in several metrics, but
Dominant signal shows drying severity, depending on model depending on model and index substantial intermodel spread and
for soil moisture, water-balance One study by Li et al. and index (Naumann et al., (Naumann et al., 2018; Xu et al., lack of signal for total soil moisture
(precipitation- (2017) concluded that 2018; Xu et al., 2019a; Gu et 2019a; Cook et al., 2020; Gu et al., (Dai et al., 2018; Cook et al., 2020;
evapotranspiration), PDSI-PM anthropogenic forcing al., 2020)(Chapter 11 2020) (Chapter 11 Supplementary Vicente-Serrano et al.,
and SPEI-PM, but with some has increased AED Supplementary Material Material (11.SM)). 2020a)(Chapter 11 Supplementary
differences between subregions and contributed to (11.SM)). Material (11.SM)).
and studies (Greve et al., 2014; drought severity over Weak signals and inconsistent
Dai and Zhao, 2017; Li et al., the last decades. Weak signals and inconsistent trends between models and Increase in drought severity based
2017c; Spinoni et al., 2019; trends between models for total subregions for total and surface soil on surface soil moisture (Dai et al.,
Padrón et al., 2020). and surface soil moisture (Xu et moisture (Xu et al., 2019a; Cook et 2018; Lu et al., 2019; Cook et al.,
al., 2019a)(Chapter 11 al., 2020)(Chapter 11 2020). (Chapter 11 Supplementary
Supplementary Material Supplementary Material (11.SM)), Material (11.SM)): only median, not
(11.SM)), but increased drying but increased drying based on SPEI- 83.5%ile), PDSI (Dai et al., 2018),
based on SPEI-PM (Naumann PM (Naumann et al., 2018; Gu et and SPEI-PM (Cook et al., 2014b;
et al., 2018; Gu et al., 2020). al., 2020). Vicente-Serrano et al., 2020a); but
increase in median response and
substantial intermodel spread for
total soil moisture (Cook et al.,
2020)(Chapter 11 Supplementary
Material (11.SM))
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Inconsistent Medium confidence: Increase of
evidence. Limited evidence evidence. One study shows trends in available studies (Touma hydrological drought severity
lack of signal (Touma et al., et al., 2015; Cook et al., 2020; Zhai (Prudhomme et al., 2014; Giuntoli et
2015). et al., 2020b) al., 2015; Touma et al., 2015; Cook
et al., 2020); but large intermodel
spread (only 2/3 of models showing
signal) (Touma et al., 2015) and
weak signal-to-noise ratio in eastern
half of domain (Giuntoli et al.,
2015).
Do Not Cite, Quote or Distribute 11-162 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
Western MET Medium confidence: Decrease Low confidence: Low confidence: Inconsistent Low confidence: Inconsistent trends Low confidence: Inconsistent trends ,
Siberia in dryness based on SPI and Limited evidence evidence in CMIP5 (Xu et al., (Chapter 11 Supplementary Material but slight decrease in some studies
(WSB) CDD, but some inconsistent 2019a) and CMIP6 projections (11.SM)) or slight decrease in drought (Touma et al., 2015; Spinoni et al.,
trends in part of domain (Zhang (Chapter 11 Supplementary
(Khlebnikova et al., 2019b; Xu et al., 2020)(Chapter 11 Supplementary
et al., 2017a, 2019b; Material (11.SM)).
Khlebnikova et al., 2019b; 2019a; Spinoni et al., 2020). Material (11.SM)).
Spinoni et al., 2019; Dunn et
al., 2020). Spinoni et al. (2020): Slight decrease
(Khlebnikova et al., 2019b): Mostly Touma et al. (2015): Tendency
Khlebnikova et al. (2019): In decrease in CDD in a regional climate towards decrease but partly lack of
part mixed signals within model driven by several CMIP5 model agreement.
domain
models (RCP8.5, 2050-2059 relative
(Dunn et al., 2020): Mostly Chapter 11 Supplementary Material
decreasing trend, including to 1990-1999)
(11.SM): Lack of model agreement
significant changes.
(Spinoni et al., 2019): Mostly Chapter 11 Supplementary Material
decreasing trends (11.SM): Tendency towards decrease
but lack of model agreement.
AGR, Low confidence: Inconsistent Low confidence: Low confidence: Inconsistent Low confidence: Inconsistent trends Low confidence: Mixed signals
ECOL trends according to subregions Limited evidence trends among different metrics among different metrics. No signal between different models and metrics,
or indices based on soil and models. Inconsistent soil with total soil moisture (Chapter 11 including total and surface soil
moisture, PDSI-PM and SPEI- moisture projections in CMIP5 Supplementary Material (11.SM)) and moisture in CMIP6 (Chapter 11
PM (Greve et al., 2014; Dai (Xu et al., 2019a) and CMIP6 SPEI-PM (Naumann et al., 2018; Gu Supplementary Material
and Zhao, 2017; Li et al., (Chapter 11 Supplementary et al., 2020), and wetting trend with (11.SM))(Cook et al., 2020), surface
2017c; Spinoni et al., 2019; Material (11.SM)), and decrease surface soil moisture (Xu et al., soil moisture in CMIP5 (Dai et al.,
Padrón et al., 2020). in drought severity based on 2019a). 2018; Lu et al., 2019), PDSI (Dai et
SPEI-PM (Naumann et al., 2018; al., 2018) and SPEI-PM (Cook et al.,
Gu et al., 2020). 2014b; Vicente-Serrano et al., 2020a).
Difference in signal in CMIP6 vs
CMIP5: CMIP6 models show drying
in soil moisture, while CMIP5 models
show wetting (Cook et al., 2020)
Do Not Cite, Quote or Distribute 11-163 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Inconsistent Low confidence: Inconsistent trends.
evidence. One study suggests Limited evidence evidence. One study shows trends in available studies (Touma et Mixed signal among studies and low
increasing weak (wetting) trend drying (Touma et al., 2015). al., 2015; Cook et al., 2020; Zhai et signal to noise ratio (Prudhomme et al.,
in runoff (Dai and Zhao, 2017). al., 2020b) 2014; Giuntoli et al., 2015; Touma et
Some increase in runoff at al., 2015; Cook et al., 2020)
stations from 1951-1990 and (Cook et al., 2020): Inconsistent
1961-2000 (Gudmundsson et trends including large seasonal
al., 2019) variations
(Zhai et al., 2020b): Inconsistent
trends in study with single
hydrological model driven with
HAPPI-MIP GCM simulations
(Touma et al., 2015): Increase in the
frequency of hydrological droughts
based on SRI in CMIP5
Eastern MET Medium confidence: Decrease Low confidence: Low confidence: Limited Medium confidence: Decrease in Medium confidence: Decrease in
Siberia in the duration and frequency Limited evidence evidence. Tendency towards frequency and severity of meteorological drought severity
(ESB) of meteorological droughts decrease in SPI in CMIP5 (Xu et meteorological droughts (Touma et al., 2015; Spinoni et al.,
(Khlebnikova et al., 2019b; al., 2019a) and CDD in CMIP6 (Khlebnikova et al., 2019b; Xu et al., 2020)(Chapter 11 Supplementary
Spinoni et al., 2019; Dunn et (Chapter 11 Supplementary 2019a; Spinoni et al., 2020)(Chapter Material (11.SM)).
al., 2020). Material (11.SM)). 11 Supplementary Material (11.SM)).
(Khlebnikova et al., 2019b):
Decrease in fraction of dry days (Khlebnikova et al., 2019b):
and decrease in mean CDD, but Projections with a regional climate
inconsistent trends for model driven with several CMIP5
maximum CDD, for 1991-2015 GCMs (RCP8.5, 2050-2059 compared
compared to 1966-1990 with 1990-1999): Mostly decrease in
CDD but increase in part of domain,
(Dunn et al., 2020): Significant in particular in the south
CDD decrease
(Spinoni et al., 2019): Mostly
decrease in SPI, but partly
mixed signals and inconsistent
trends
Do Not Cite, Quote or Distribute 11-164 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
AGR, Low confidence: Inconsistent Low confidence: Low confidence: Mixed signal in Low confidence: Mixed signal in Low confidence: Mixed signal in
ECOL trends depending on subregion Limited evidence changes in drought severity changes in drought severity depending drought changes depending on models
and index based on soil depending on models and on models and metrics. and metrics, including total and
moisture, PDSI-PM and SPEI- metrics. surface soil moisture in CMIP6
PM (Greve et al., 2014; Dai Inconsistent trends in soil moisture (Chapter 11 Supplementary Material
and Zhao, 2017; Spinoni et al., Inconsistent trends in soil (Xu et al., 2019a)(Chapter 11 (11.SM))(Cook et al., 2020), surface
2019; Padrón et al., 2020). moisture (Xu et al., Supplementary Material (11.SM)) , soil moisture in CMIP5 (Dai et al.,
2019a)(Chapter 11 but wetting tendency for SPEI-PM 2018; Lu et al., 2019), PDSI (Dai et
Supplementary Material (Naumann et al., 2018; Gu et al., al., 2018) and SPEI-PM (Cook et al.,
(11.SM)), , but wetting tendency 2020). 2014b; Vicente-Serrano et al., 2020a).
for SPEI-PM (Naumann et al.,
2018; Gu et al., 2020). Difference in signal in CMIP6 vs
CMIP5: CMIP6 models show drying
in soil moisture, while CMIP5 models
show wetting (Cook et al., 2020)
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Inconsistent Low confidence: Mixed signal among
evidence. One study suggests Limited evidence evidence. One study shows lack trends in available studies (Touma et studies (Prudhomme et al., 2014;
increasing (wetting) trend in of signal (Touma et al., 2015). al., 2015; Cook et al., 2020; Zhai et Giuntoli et al., 2015; Touma et al.,
runoff (Dai and Zhao, 2017). al., 2020b) 2015; Cook et al., 2020)
Some increase in runoff at
stations from 1951-1990 and (Cook et al., 2020): Inconsistent
1961-2000 (Gudmundsson et trends including large seasonal
al., 2019) variations
(Zhai et al., 2020b): Inconsistent
trends in one study based on single
hydrological model driven by HAPPI-
MIP GCM simulations
(Touma et al., 2015): Mixed signal.
Russian MET Low confidence: Mixed Low confidence: Low confidence: Limited Medium confidence: Decrease Medium confidence: Decrease in
Far East signals between subregions and Limited evidence. evidence. Weak decrease in (Khlebnikova et al., 2019b; Xu et al., drought severity (Touma et al., 2015;
(RFE) studies (Knutson and Zeng, One study, Wilcox et available analyses (Xu et al., 2019a)(Chapter 11 Supplementary Han et al., 2018; Spinoni et al.,
2018; Khlebnikova et al., al. in (Herring et al., 2019a)(Chapter 11 Material (11.SM)).
2020)(Chapter 11 Supplementary
2019b; Spinoni et al., 2019; 2015), but mostly Supplementary Material (Khlebnikova et al., 2019b): Regional
Dunn et al., 2020). inconclusive. (11.SM)). climate model driven by several Material (11.SM)).
CMIP5 models (RCP8.5, 2050-2059
relative to 1990-1999): Mostly
decrease in CDD but also increase in
part of region (Kamtchatka
Peninsual).
Do Not Cite, Quote or Distribute 11-165 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
AGR, Low confidence: Inconsistent Low confidence: Low confidence: Inconsistent Low confidence: Inconsistent trends Low confidence: Mixed signals
ECOL trends depending on subregion Limited evidence trends depending on model and depending on model and index. between different models and metrics,
based on soil moisture, PDSI- index. including CMIP6 total and surface soil
PM and SPEI-PM (Greve et al., Inconsistent trends in CMIP6 total moisture (Chapter 11 Supplementary
2014; Dai and Zhao, 2017; Inconsistent trends in total and and surface soil moisture (Chapter 11 Material (11.SM))(Cook et al., 2020),
Spinoni et al., 2019; Padrón et surface soil moisture in CMIP6 Supplementary Material and CMIP5-based surface soil
al., 2020). (Chapter 11 Supplementary (11.SM))(Cook et al., 2020), but moisture (Dai et al., 2018; Lu et al.,
Material (11.SM)), but wetting wetting trends from CMIP5-based 2019), PDSI (Dai et al., 2018) and
trends from CMIP5-based surface surface soil moisture (Xu et al., SPEI-PM (Cook et al., 2014b; Vicente-
soil moisture (Xu et al., 2019a) 2019a) and SPEI-PM (Naumann et Serrano et al., 2020a).
and SPEI-PM (Naumann et al., al., 2018; Gu et al., 2020).
2018; Gu et al., 2020). Difference in signal in CMIP6 vs
CMIP5: CMIP6 models show drying
in soil moisture, while CMIP5 models
(Naumann et al., 2018) : EC- show wetting (Cook et al., 2020).
Earth driven by SSTs from
several CMIP5 models.
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Inconsistent Low confidence: Inconsistent signal
evidence. One study suggests Limited evidence evidence. One study shows lack trends. Available studies show among studies and metrics, with
decreasing (drying) trend in of signal (Touma et al., 2015). inconsistent signal with high seasonal generally weak drying trend in summer
runoff (Dai and Zhao, 2017). variations (Cook et al., 2020) or weak season (Prudhomme et al., 2014;
signal (Touma et al., 2015; Zhai et al., Giuntoli et al., 2015; Touma et al.,
2020b). 2015; Cook et al., 2020)
East Asia MET Low confidence: Lack of signal Low confidence: Low confidence: Limited Low confidence: Inconsistent Low confidence: Inconsistent
(EAS) and mixed trends between Limited evidence evidence. Inconsistent trends depending on model, region trends between different models and
subregions (Spinoni et al., (Qin et al., 2015a; subregional trends (Xu et al., or index (Guo et al., 2018; Xu et al., important spatial variability (Zhou et
2019; Zhang et al., 2019a; Herring et al., 2019). 2019a) or drying tendency 2019a; Spinoni et al., al., 2014; Touma et al., 2015;
Dunn et al., 2020; Li et al., (Chapter 11 Supplementary 2020)(Chapter 11 Supplementary Kusunoki, 2018a; Spinoni et al.,
2020b). Drying trends in Material (11.SM)). Material (11.SM)). 2020)(Chapter 11 Supplementary
Southwestern China (Qin et al., Material (11.SM)).
2015a) and Northern China (Spinoni et al., 2020): Tendency
(Qin et al., 2015b), but not for towards decreased in drought (Zhou et al., 2014): Tendency
overall China (Li et al., 2020b). severity based on SPI. towards wetting in the north and
drying in the south based on CDD.
(Huang et al., 2018a): Important
subregional differences in SPI (Kusunoki, 2018a): Increasing CDD
projections in a single GCM (drying trend) over Japan based on
one GCM.
Chapter 11 Supplementary Material
(11.SM): Tendency towards drying
based on CDD (increasing CDD),
but inconsistent trends depending
on model.
(Xu et al., 2019a): Inconsistent
subregional trends based on SPI.
Do Not Cite, Quote or Distribute 11-166 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
AGR, Low confidence: Low confidence: Inconsistent Low confidence: Mixed signals Medium confidence: Increasing
ECOL Medium confidence: Increase Limited evidence. trends depending on model, depending on model, subregion and dryness as dominant signal in
in drying, especially since ca. subregion and index (Huang et index (Gao et al., 2017b; Naumann projections and over larger part of
1990; but wetting tendency Zhang et al. (2020) al., 2018a; Naumann et al., et al., 2018; Xu et al., 2019a; Cook domain, but also inconsistent signal
beforehand and partly concluded that 2018; Xu et al., 2019a; Gu et et al., 2020; Gu et al., for some indices and part of the
inconsistent subregional trends. anthropogenic forcing al., 2020)(Chapter 11 2020)(Chapter 11 Supplementary domain (Cook et al., 2014b, 2020;
Large-scale studies based on contributed to 2018 Supplementary Material Material (11.SM)). Cheng et al., 2015; Dai et al., 2018;
observed soil moisture, drought, principally as (11.SM)) . Naumann et al., 2018; Lu et al.,
modelled soil moisture or water consequence of (Gao et al., 2017b): Study for very 2019; Vicente-Serrano et al.,
balance driven by enhanced AED. (Huang et al., 2018a): small region (Loess Plateau). 2020a)(Chapter 11 Supplementary
meteorological observations, Inconsistent projections in a Material (11.SM)).
and SPEI-PM, show drying in One study suggests study with a single GCM for
northern part of domain that soil moisture the time frame 2016-2050 (for
(northern China, Russian part drought conditions in different scenarios) compared
of domain, Japan) as well as in northern China have to 1960-2005, i.e corresponding
Southwest China (east of been intensified by to 1.5°C projections compared
Tibetan Plateau), but there are agriculture (Liu et al., to recent past.
some inconsistent trends in part 2015).
of region or some studies, as
well as for different time
frames (Greve et al., 2014;
Chen and Sun, 2015b; Cheng et
al., 2015; Qiu et al., 2016; Dai
and Zhao, 2017; Jia et al.,
2018; Spinoni et al., 2019; Li et
al., 2020b; Padrón et al., 2020).
Identified trends are also
confirmed by regional studies
(Liu et al., 2015; Qin et al.,
2015b; Liang et al., 2020;
Wang et al., 2020). Most of the
drying trend took place since
1990, with wetting trend
beforehand (Chen and Sun,
2015b; Wu et al., 2020b).
Do Not Cite, Quote or Distribute 11-167 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
HYDR Medium confidence: Increase Low confidence: Low confidence: Limited Low confidence: Limited Low confidence: Inconsistent trend
in hydrological drought in the Limited evidence and evidence. One study shows evidence and inconsistent trends between models and studies, and
region, in particular in northern mixed signals. lack of signal (Touma et al., in available studies (Touma et al., generally low signal-to-noise ratio
China; inconsistent trends in Available evidence 2015). 2015; Cook et al., 2020; Zhai et al., (Prudhomme et al., 2014; Giuntoli et
part of the region (Liu et al., suggests that a 2020b). al., 2015; Touma et al., 2015; Cook
2015; Dai and Zhao, 2017; combination of change et al., 2020)
Zhang et al., 2018b). in climatic drivers
(precipitation, Epot) (Touma et al., 2015; Cook et al.,
Drying in large part of domain, and human drivers 2020): Generally inconsistent trends
in particular in northern China (agriculture, water between models, with low model
(Zhao and Dai, 2017) management) are agreement.
responsible for trends
Increase of hydrological (Liu et al., 2015; (Giuntoli et al., 2015): Trend
droughts in the Yangtze river Zhang et al., 2018b). towards drying but generally low
(Zhang et al., 2018b). signal-to-noise ratio except in small
Increasing subregion.
hydrological droughts
trends in the Yangtze
river are dominantly
driven by
precipitation, but
increases in potential
evaporation and
human activities also
play a role (Zhang et
al., 2018b). Drought
conditions in northern
China (soil moisture
and runoff) have been
intensified by
agriculture (Liu et al.,
2015).
Do Not Cite, Quote or Distribute 11-168 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
Eastern MET Low confidence: Inconsistent Low confidence: Low confidence. Limited Medium confidence: Decrease in Medium confidence: Decrease in
Central trends between subregions, Limited evidence evidence; slight decrease in drought severity, with weakly drought severity (Touma et al., 2015;
Asia with overall tendency to meteorological drought in inconsistent changes for some Spinoni et al., 2020)(Chapter 11
(ECA) decrease (Spinoni et al., 2019; available analyses (Xu et al., indices (Xu et al., 2019a; Spinoni et Supplementary Material (11.SM)).
Dunn et al., 2020). 2013) (Chapter 11 al., 2020)(Chapter 11
Supplementary Material Supplementary Material (11.SM))
(11.SM))
(Spinoni et al., 2019): Strong
decrease in drought for SPI-based
metrics in RCP4.5 compared to
1981-2010
(Xu et al., 2019a): Decrease in
frequency of SPI-based events but
slight increase or inconsistent
changes in duration of SPI-based
events.
Chapter 11 Supplementary Material
(11.SM): substantial decrease in
CDD
AGR, Medium confidence: Increase Low confidence: Low confidence: Mixed signal Low confidence: Mixed signal in Low confidence: Mixed trends
ECOL in drying, but some conflicting Limited evidence in changes in drought severity, changes in drought severity. between different models and
trends between drought metrics lack of signal based in total Inconsistent trends in total and drought metrics (Chapter 11
and sub-regions (Greve et al., column soil moisture (Xu et al., surface soil moisture, with stronger Supplementary Material
2014; Cheng et al., 2015; Dai 2019a)(Chapter 11 tendency to wetting, (Xu et al., (11.SM))(Cook et al., 2014b, 2020;
and Zhao, 2017; Li et al., Supplementary Material 2019a; Cook et al., 2020)(Chapter Dai et al., 2018; Lu et al., 2019;
2017c; Spinoni et al., 2019; (11.SM)) and SPEI-PM 11 Supplementary Material Vicente-Serrano et al., 2020a).
Padrón et al., 2020; Zhang et (Naumann et al., 2018; Gu et (11.SM)), and drying based on the
al., 2020c). al., 2020). SPEI-PM (Naumann et al., 2018;
Gu et al., 2020).
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited evidence Low confidence: Mixed trends.
evidence. Mostly inconsistent Limited evidence evidence. One study shows and inconsistent trends (Touma et Model disagreement and inconsistent
trends in one study (Dai and lack of signal (Touma et al., al., 2015; Cook et al., 2020; Zhai et changes among studies, seaons and
Zhao, 2017). 2015). al., 2020b) metrics, with overall low signal-to-
noise ratio (Prudhomme et al., 2014;
Giuntoli et al., 2015; Touma et al.,
2015; Cook et al., 2020).
Do Not Cite, Quote or Distribute 11-169 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
Tibetan MET Low confidence: Inconsistent Low confidence: Low confidence: Limited Low confidence: Inconsistent Low confidence: Inconsistent
Plateau trends (Jiang et al., 2013; Donat Limited evidence evidence. Weak or inconsistent trends, but tendency towards trends between models, but
(TIB) et al., 2016a; Hu et al., 2016; trends in available analyses (Xu wetting (Xu et al., 2019a; Cook et tendency towards wetting and
Dunn et al., 2020). et al., 2019a)(Chapter 11 al., 2020)(Chapter 11 decrease in drought (Zhou et al.,
Supplementary Material Supplementary Material (11.SM)) 2014; Touma et al., 2015)(Chapter
(11.SM)) 11 Supplementary Material
(Spinoni et al., 2019): No data in (11.SM)).
the region
(Cook et al., 2020): Only analysis (Zhou et al., 2014): Decrease of
of mean precipitation but tendency CDD is projected but there is large
towards wetting in all seasons in the uncertainty
region
(Xu et al., 2019a)(Chapter 11
Supplementary Material (11.SM)):
Weak trends but tendency towards
wetting.
AGR, Low confidence: Inconsistent Low confidence: Low confidence: Inconsistent Low confidence: Inconsistent Low confidence: Inconsistent
ECOL trends. Spatially varying Limited evidence trends between models, indices trends between models, indices and trends between models, indices and
trends, with slight tendency to and subregions (Naumann et subregions (Naumann et al., 2018; subregions (Cook et al., 2014b,
overall wetting(Cheng et al., al., 2018; Xu et al., 2019a; Gu Xu et al., 2019a; Cook et al., 2020; 2020; Dai et al., 2018; Lu et al.,
2015; Dai and Zhao, 2017; Jia et al., 2020)(Chapter 11 Gu et al., 2020)(Chapter 11 2019; Vicente-Serrano et al.,
et al., 2018; Zhang et al., Supplementary Material Supplementary Material (11.SM)). 2020a)(Chapter 11 Supplementary
2018a; Li et al., 2020c; Wang (11.SM)). Material (11.SM)).
et al., 2020).
(Greve et al., 2014; Spinoni et
al., 2019; Padrón et al., 2020):
Missing data in most of region.
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited Low confidence: Inconsistent
evidence. Limited evidence evidence. One study shows evidence and inconsistent trends trends between models and studies,
lack of signal (Touma et al., in available studies (Touma et al., and low signal-to-nois ratio
2015). 2015; Cook et al., 2020; Zhai et al., (Prudhomme et al., 2014; Giuntoli et
2020b) al., 2015; Touma et al., 2015; Cook
et al., 2020)
Do Not Cite, Quote or Distribute 11-170 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
South MET Medium confidence: Increase Low confidence: Low confidence: Limited Low confidence: Inconsistent Low confidence: Inconsistent
Asia in meteorological drought. Limited evidence evidence and inconsistent trends, with light tendency to trends depending on model and
(SAS) Subregional differences but (Fadnavis et al., 2019) trends (Xu et al., decreased drying (Xu et al., 2019a; subregion, with light tendency to
drying is dominant (Mishra et 2019a)(Chapter 11 Spinoni et al., 2020)(Chapter 11 decreases in meteorological drought
al., 2014b; Malik et al., 2016; Supplementary Material Supplementary Material (11.SM)) in CMIP5 and CMIP6 (Mishra et al.,
Guhathakurta et al., 2017; (11.SM)). 2014b; Touma et al., 2015; Salvi and
Spinoni et al., 2019; Dunn et Ghosh, 2016; Spinoni et al.,
al., 2020) (see also Section 2020)(Chapter 11 Supplementary
10.6.3) Material (11.SM)); light increased
drying in NDD in CORDEX-CORE
(Coppola et al., 2021b). Overall poor
climate model performance for
South Asia monsoon in CMIP5 and
CORDEX (Mishra et al., 2014a;
Saha et al., 2014; Sabeerali et al.,
2015; Singh et al., 2017b). See also
Section 10.6.3 for assessment for
changes in Indian summer monsoon
rainfall.
AGR, Low confidence: Lack of Low confidence: Low confidence: Inconsistent Low confidence: Inconsistent Medium confidence: Decreased
ECOL signal and inconsistent trends Limited evidence trends in drought between trends in drought between drying trend (Chapter 11
depending on subregion based models and subregions models, subregions and studies, Supplementary Material
on soil moisture, PDSI-PM and (Naumann et al., 2018; Xu et but slight dominant tendency (11.SM))(Cook et al., 2014b, 2020;
SPEI-PM (Greve et al., 2014; al., 2019a; Gu et al., towards wetting(Naumann et al., Mishra et al., 2014b; Dai et al.,
Mishra et al., 2014b; Dai and 2020)(Chapter 11 2018; Xu et al., 2019a; Cook et al., 2018; Lu et al., 2019; Vicente-
Zhao, 2017; Spinoni et al., Supplementary Material 2020; Gu et al., Serrano et al., 2020a)(Chapter 11
2019; Padrón et al., 2020) and (11.SM)) 2020)(CMIP6.ANNEX-CH11) Supplementary Material (11.SM))
decrease of the drying effect of
the atmospheric evaporative
demand (Jhajharia et al., 2015).
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited Low confidence: Inconsistent
evidence. Inconsistent trends or Limited evidence evidence. One study shows evidence. Lack of signal in CMIP5 trends between models and studies
limited data in available studies lack of signal (Touma et al., (Touma et al., 2015). Decrease in (Prudhomme et al., 2014; Giuntoli et
(Zhao and Dai, 2017; 2015). dryness in CMIP6 (Cook et al., al., 2015; Touma et al., 2015; Cook
Gudmundsson et al., 2019, 2020); mostly inconsistent trends in et al., 2020)
2021). HAPPI-MIP driven simulations
with one hydrological model (Zhai
et al., 2020b).
Do Not Cite, Quote or Distribute 11-171 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
Southeast MET Low confidence: Inconsistent Low confidence: Low confidence: Limited Low confidence: Inconsistent Medium confidence: Increase in
Asia trends between subregions Limited evidence evidence (Xu et al., trends between models, subregions drying in CMIP6 and CORDEX
(SEA) (Spinoni et al., 2019; Dunn et (Mcbride et al., 2015; 2019a)(Chapter 11 and studies (Tangang et al., 2018; simulations (Cook et al., 2020;
al., 2020). King et al., 2016b) Supplementary Material Xu et al., 2019a; Spinoni et al., Supari et al., 2020; Coppola et al.,
although the the (11.SM)) 2020)(Chapter 11 Supplementary 2021b) (Chapter 11 Supplementary
equatorial Asia Material (11.SM)) but with overall Material (11.SM)). but inconsistent
drought of 2015 has drying in CMIP6 and CORDEX trends or wetting in CMIP5-based
been attributed to simulations (Tangang et al., 2018; projections(Touma et al., 2015;
anthropogenic Cook et al., 2020; Coppola et al., Cook et al., 2020; Spinoni et al.,
warming effects 2021b) (Chapter 11 Supplementary 2020; Supari et al., 2020)(
(Shiogama et al., Material (11.SM)).
2020). (Supari et al., 2020): Strong drying
(Tangang et al., 2018): Projected trend based on CDD in CORDEX
drying based on CDD in CORDEX simulations for Indonesia
simulations for Indonesia
(Coppola et al., 2021b): Drying
(Xu et al., 2019a): Inconsistent based on number of dry days (NDD)
trends across region based on SPI, in CORDEX-CORE projects
but with slight drying over
Indonesia (Cook et al., 2020): Decreasing trend
in mean precipitation which is only
(Spinoni et al., 2020): Wetting trend found in CMIP6 and not in CMIP5.
based on SPI
Chapter 11 Supplementary Material
(Chapter 11 Supplementary (11.SM): Strong projected drying
Material (11.SM)): Drying trend trend based on CDD in CMIP6
based on CDD projections
(Touma et al., 2015): Inconsistent
trends in SPI in CMIP5 projections
(Spinoni et al., 2020): Wetting trend
based on SPI in CMIP5 projections.
(Cai et al., 2014a, 2015, 2018): An
increasing frequency of precipitation
deficits is projected as a
consequence of an increasing
frequency of extreme El Niño.
Do Not Cite, Quote or Distribute 11-172 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
AGR, Low confidence: Inconsistent Low confidence: Low confidence: Inconsistent Low confidence: Inconsistent Low confidence: Mixed signal
ECOL trends depending onsubregion Limited evidence trends depending on model, trends depending on model, depending on model and metric.
and index based on soil subregion, index or study subregion, index or study Drying tendency based on CMIP6
moisture, PDSI-PM and SPEI- (Naumann et al., 2018; Xu et (Naumann et al., 2018; Xu et al., soil moisture projections (Cook et
PM (Greve et al., 2014; Dai al., 2019a; Gu et al., 2019a; Cook et al., 2020; Gu et al., al., 2020)(Chapter 11 Supplementary
and Zhao, 2017; Spinoni et al., 2020)(Chapter 11 2020)(Chapter 11 Supplementary Material (11.SM)), inconsistent
2019; Padrón et al., 2020). Supplementary Material Material (11.SM)). trends in CMIP5 surface soil
(11.SM)) moisture (Dai et al., 2018; Lu et al.,
2019), but wetting trends with PDSI
(Dai et al., 2018) and SPEI-PM
(Cook et al., 2014b; Vicente-Serrano
et al., 2020a) in studies driven with
CMIP5 data. ,
(Cook et al., 2020): Drying trend in
SEA in CMIP6), but not in CMIP5.
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited Low confidence: Inconsistent trend
evidence. Regionally Limited evidence evidence. One study shows evidence and inconsistent trends between models and studies
inconsistent trends in one study decrease in hydrological in available studies (Touma et al., (Prudhomme et al., 2014; Giuntoli et
(Dai and Zhao, 2017). drought (Touma et al., 2015). 2015; Cook et al., 2020; Zhai et al., al., 2015; Touma et al., 2015; Cook
2020b) et al., 2020)
1
2 [END TABLE 11.9 HERE]
3
4
5 [START TABLE 11.10 HERE]
6
7 Table 11.10: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for temperature extremes in Australasia,
8 subdivided by AR6 regions. See Sections 11.9.1 and 11.9.2 for details.
Detection and attribution; Projections
Region Observed trends
event attribution 1.5 °C 2 °C 4 °C
All Australasia Significant increases in the Robust evidence of a human CMIP6 models project a CMIP6 models project a CMIP6 models project a
intensity and frequency of hot contribution to the observed robust increase in the robust increase in the robust increase in the
extremes and decreases in the increase in the intensity and intensity and frequency of intensity and frequency of intensity and frequency of
intensity and frequency of frequency of hot extremes TXx events and a robust TXx events and a robust TXx events and a robust
cold extremes (CSIRO and and decrease in the intensity decrease in the intensity and decrease in the intensity and decrease in the intensity and
BOM, 2015; Jakob and and frequency of cold frequency of TNn events (Li frequency of TNn events (Li frequency of TNn events (Li
Walland, 2016; Alexander extremes (Seong et al., 2020; et al., 2020). Median increase et al., 2020). Median increase et al., 2020). Median increase
and Arblaster, 2017) Hu et al., 2020; Wang et al., of more than 0C in the 50- of more than 0.5°C in the 50- of more than 2.5°C in the 50-
2017). year TXx and TNn events year TXx and TNn events year TXx and TNn events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020) level (Li et al., 2020) level (Li et al., 2020)
Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
Do Not Cite, Quote or Distribute 11-173 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
increase in the intensity and increase in the intensity and increase in the intensity and
frequency of hot extremes frequency of hot extremes frequency of hot extremes
and decrease in the intensity and decrease in the intensity and decrease in the intensity
and frequency of cold and frequency of cold and frequency of cold
extremes (Alexander and extremes (Alexander and extremes (Alexander and
Arblaster, 2017; Herold et al., Arblaster, 2017; Herold et al., Arblaster, 2017; Herold et al.,
2018; Evans et al., 2020; 2018; Evans et al., 2020; 2018; Evans et al., 2020;
Grose et al., 2020) Grose et al., 2020) Grose et al., 2020)
Very likely increase in the Human influence very likely Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contributed to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Very likely (compared with Extremely likely (compared Virtually certain (compared
intensity and frequency of frequency of hot extremes the recent past (1995-2014)) with the recent past (1995- with the recent past (1995-
cold extremes and decrease in the intensity Extremely likely (compared 2014)) 2014))
and frequency of cold with pre-industrial) Virtually certain (compared Virtually certain (compared
with pre-industrial) with pre-industrial)
extremes
Decrease in the intensity and
frequency of cold extremes: Decrease in the intensity and Decrease in the intensity and
Very likely (compared with frequency of cold extremes: frequency of cold extremes:
the recent past (1995-2014)) Extremely likely (compared Virtually certain (compared
Extremely likely (compared with the recent past (1995- with the recent past (1995-
with pre-industrial) 2014)) 2014))
Virtually certain (compared Virtually certain (compared
with pre-industrial) with pre-industrial)
Northern Australia (NAU) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Perkins and Alexander, and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
2013; Wang et al., 2013c; extremes (Wang et al., 2017; increase of more than 0C in the Median increase of more than Median increase of more than
CSIRO and BOM, 2015; Hu et al., 2020; Seong et al., 50-year TXx and TNn events 0.5°C in the 50-year TXx and 3°C in the 50-year TXx and
Donat et al., 2016a; 2020; Knutson et al., 2014; compared to the 1°C warming TNn events compared to the TNn events compared to the
Alexander and Arblaster, Lewis and Karoly, 2014; level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
2017; Dunn et al., 2020) Perkins et al., 2014; Arblaster than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 3.5°C in
et al., 2014; Hope et al., TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
2015, 2016; Perkins and (Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Gibson, 2015)
Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity and increase in the intensity and increase in the intensity and
frequency of hot extremes and frequency of hot extremes and frequency of hot extremes and
decrease in the intensity and decrease in the intensity and decrease in the intensity and
frequency of cold extremes frequency of cold extremes frequency of cold extremes
(Alexander and Arblaster, 2017; (Alexander and Arblaster, 2017; (Alexander and Arblaster, 2017;
Herold et al., 2018; Evans et al., Herold et al., 2018; Evans et al., Herold et al., 2018; Evans et al.,
2020; Grose et al., 2020) 2020; Grose et al., 2020) 2020; Grose et al., 2020)
High confidence in the High confidence in a human Increase in the intensity and Increase in the intensity and Increase in the intensity and
increase in the intensity and contribution to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
Do Not Cite, Quote or Distribute 11-174 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
frequency of hot extremes increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
and likely decrease in the frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
intensity and frequency of and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
cold extremes and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial) with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Central Australia (CAU) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Perkins and Alexander, and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
2013; Wang et al., 2013c; extremes ((Wang et al., increase of more than 0C in the Median increase of more than Median increase of more than
CSIRO and BOM, 2015; 2017c; Hu et al., 2020; Seong 50-year TXx and TNn events 0.5°C in the 50-year TXx and 2.5°C in the 50-year TXx and
Donat et al., 2016a; et al., 2020; Knutson et al., compared to the 1°C warming TNn events compared to the TNn events compared to the
Alexander and Arblaster, 2014; Lewis and Karoly, level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
2017; Dunn et al., 2020) 2014; Perkins et al., 2014; than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 4°C in
Arblaster et al., 2014; Hope et TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
al., 2015, 2016; Perkins and (Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Gibson, 2015; King et al.,
2014) Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity and increase in the intensity and increase in the intensity and
frequency of hot extremes and frequency of hot extremes and frequency of hot extremes and
decrease in the intensity and decrease in the intensity and decrease in the intensity and
frequency of cold extremes frequency of cold extremes frequency of cold extremes
(Alexander and Arblaster, 2017; (Alexander and Arblaster, 2017; (Alexander and Arblaster, 2017;
Herold et al., 2018; Evans et al., Herold et al., 2018; Evans et al., Herold et al., 2018; Evans et al.,
2020; Grose et al., 2020) 2020; Grose et al., 2020) 2020; Grose et al., 2020)
Likely increase in the Human influence likely Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contributed to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
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Final Government Distribution Chapter11 IPCC AR6 WGI
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial) with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Eastern Australia (EAU) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Perkins and Alexander, and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
2013; Wang et al., 2013c; extremes ((Wang et al., increase of more than 0.5°C in Median increase of more than Median increase of more than
CSIRO and BOM, 2015; 2017c; Hu et al., 2020; Seong the 50-year TXx and TNn events 0.5°C in the 50-year TXx and 2.5°C in the 50-year TXx and
Donat et al., 2016a; et al., 2020; Knutson et al., compared to the 1°C warming TNn events compared to the TNn events compared to the
Alexander and Arblaster, 2014; Lewis and Karoly, level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
2017; Dunn et al., 2020) 2014; Perkins et al., 2014; than 1°C in annual TXx and TNn 2020) and more than 1.5°C in 2020) and more than 3.5°C in
Arblaster et al., 2014; Hope et compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
al., 2015, 2016; Perkins and (Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Gibson, 2015; King et al.,
2015) Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity and increase in the intensity and increase in the intensity and
frequency of hot extremes and frequency of hot extremes and frequency of hot extremes and
decrease in the intensity and decrease in the intensity and decrease in the intensity and
frequency of cold extremes frequency of cold extremes frequency of cold extremes
(Alexander and Arblaster, 2017; (Alexander and Arblaster, 2017; (Alexander and Arblaster, 2017;
Herold et al., 2018; Evans et al., Herold et al., 2018; Evans et al., Herold et al., 2018; Evans et al.,
2020; Grose et al., 2020) 2020; Grose et al., 2020) 2020; Grose et al., 2020)
Likely increase in the Human influence likely Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contributed to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial) with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Southern Australia (SAU) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
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Final Government Distribution Chapter11 IPCC AR6 WGI
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Perkins and Alexander, and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
2013; Wang et al., 2013c; extremes (Wang et al., 2017c; increase of more than 0C in the Median increase of more than Median increase of more than
Dittus et al., 2014; CSIRO Hu et al., 2020; Seong et al., 50-year TXx and TNn events 0.5°C in the 50-year TXx and 2°C in the 50-year TXx and
and BOM, 2015; Crimp et al., 2020; Black and Karoly, compared to the 1°C warming TNn events compared to the TNn events compared to the
2016; Donat et al., 2016a; 2016; Knutson et al., 2014; level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
Alexander and Arblaster, Lewis and Karoly, 2014; than 1°C in annual TXx and TNn 2020) and more than 1.5°C in 2020) and more than 2.5°C in
2017; Dunn et al., 2020) Perkins et al., 2014; Arblaster compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
et al., 2014; Hope et al., 2015, (Annex). to pre-industrial (Annex). to pre-industrial (Annex).
2016; Perkins and Gibson,
2015) Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity and increase in the intensity and increase in the intensity and
frequency of hot extremes and frequency of hot extremes and frequency of hot extremes and
decrease in the intensity and decrease in the intensity and decrease in the intensity and
frequency of cold extremes frequency of cold extremes frequency of cold extremes
(Alexander and Arblaster, 2017; (Alexander and Arblaster, 2017; (Alexander and Arblaster, 2017;
Herold et al., 2018; Evans et al., Herold et al., 2018; Evans et al., Herold et al., 2018; Evans et al.,
2020; Grose et al., 2020) 2020; Grose et al., 2020) 2020; Grose et al., 2020)
Likely increase in the Human influence likely Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contributed to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial) with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
New Zealand (NZ) Significant increases in the Limited evidence (Seong et CMIP6 models project an CMIP6 models project a CMIP6 models project a
intensity and frequency of hot al., 2020; Wang et al., 2017) increase in the intensity and robust increase in the robust increase in the
extremes and significant frequency of TXx events and intensity and frequency of intensity and frequency of
decreases in the intensity and a decrease in the intensity and TXx events and a robust TXx events and a robust
frequency of cold extremes frequency of TNn events (Li decrease in the intensity and decrease in the intensity and
(Caloiero, 2017; Dunn et al., et al., 2020; Annex). Median frequency of TNn events (Li frequency of TNn events (Li
2020; Ministry for the increase of more than 0C in et al., 2020; Annex). Median et al., 2020; Annex). Median
Environment & Stats NZ, the 50-year TXx and TNn increase of more than 0.5°C increase of more than 2°C in
2020; Harrington, 2020) events compared to the 1°C in the 50-year TXx and TNn the 50-year TXx and TNn
warming level (Li et al., events compared to the 1°C events compared to the 1°C
2020) and more than 1°C in warming level (Li et al., warming level (Li et al.,
annual TXx and TNn 2020) and more than 1.5°C in 2020) and more than 3°C in
compared to pre-industrial annual TXx and TNn annual TXx and TNn
(Annex). compared to pre-industrial compared to pre-industrial
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Final Government Distribution Chapter11 IPCC AR6 WGI
(Annex). (Annex).
Likely increase in the Low confidence Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the High confidence (compared Likely (compared with the Extremely likely (compared
intensity and frequency of with the recent past (1995- recent past (1995-2014)) with the recent past (1995-
cold extremes 2014)) Very likely (compared with 2014))
Likely (compared with pre- pre-industrial) Virtually certain (compared
industrial) with pre-industrial)
Decrease in the intensity and
Decrease in the intensity and frequency of cold extremes: Decrease in the intensity and
frequency of cold extremes: Likely (compared with the frequency of cold extremes:
High confidence (compared recent past (1995-2014)) Extremely likely (compared
with rthe recent past (1995- Very likely (compared with with the recent past (1995-
2014)) pre-industrial) 2014))
Likely (compared with pre- Virtually certain (compared
industrial) with pre-industrial)
1 [END TABLE 11.10 HERE]
2
3
4 [START TABLE 11.11 HERE]
5
6 Table 11.11: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for heavy precipitation in Australasia,
7 subdivided by AR6 regions. See Sections 11.9.1 and 11.9.3 for details.
Region Observed trends Detection and attribution; Projections
event attribution 1.5 °C 2 °C 4 °C
All Australasia Limited evidence (Jakob and Limited evidence CMIP6 models project CMIP6 models project an CMIP6 models project a
Walland, 2016; Guerreiro et inconsistent changes in the increase in the intensity and robust increase in the
al., 2018b; Dey et al., 2019b; region (Li et al., 2020a) frequency of heavy intensity and frequency of
Dunn et al., 2020; Sun et al., precipitation (Li et al., 2020). heavy precipitation (Li et al.,
2020) Median increase of more than 2020a). Median increase of
4% in the 50-year Rx1day more than 10% in the 50-year
and Rx5day events compared Rx1day and Rx5day events
to the 1°C warming level (Li compared to the 1°C warming
et al., 2020a) level (Li et al., 2020a)
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Low confidence (compared Medium confidence Likely (compared with the
with the recent past (1995- (compared with the recent recent past (1995-2014))
2014)) past (1995-2014)) Very likely (compared with
Medium confidence Likely (compared with pre- pre-industrial)
(compared with pre- industrial)
industrial)
Northern Australia (NAU) Intensification of heavy Limited evidence (Dey et al., CMIP6 models project CMIP6 models project an CMIP6 models project a robust
Do Not Cite, Quote or Distribute 11-178 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
precipitation (Donat et al., 2019a) inconsistent changes in the increase in the intensity and increase in the intensity and
2016a; Alexander and region (Li et al., 2020a) frequency of heavy frequency of heavy
Arblaster, 2017; Evans et al., precipitation (Li et al., 2020; precipitation (Li et al., 2020;
2017; Dey et al., 2019b; Dunn Annex). Median increase of Annex). Median increase of
et al., 2020; Sun et al., 2020) more than 4% in the 50-year more than 10% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and more level (Li et al., 2020a) and more
than 6% in annual Rx1day and than 20% in annual Rx1day and
Rx5day and 2% in annual Rx5day and 10% in annual
Rx30day compared to pre- Rx30day compared to pre-
industrial (Annex). industrial (Annex).
Medium confidence in the Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
intensitification of heavy precipitation: precipitation: precipitation:
precipitation Low confidence (compared with Medium confidence (compared Likely (compared with the
the recent past (1995-2014)) with the recent past (1995- recent past (1995-2014))
Medium confidence (compared 2014)) Very likely (compared with pre-
with pre-industrial) High confidence (compared industrial)
with pre-industrial)
Central Australia (CAU) Limited evidence (Donat et Limited evidence CMIP6 models project CMIP6 models project an CMIP6 models project a robust
al., 2016a; Alexander and inconsistent changes in the increase in the intensity and increase in the intensity and
Arblaster, 2017; Evans et al., region (Li et al., 2020a) frequency of heavy frequency of heavy
2017; Dey et al., 2019b; Dunn precipitation (Li et al., 2020; precipitation (Li et al., 2020;
et al., 2020; Sun et al., 2020). Annex). Median increase of Annex). Median increase of
more than 4% in the 50-year more than 10% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and more level (Li et al., 2020a) and more
than 4% in annual Rx1day and than 10% in annual Rx1day and
Rx5day and 2% in annual Rx5day and 4% in annual
Rx30day compared to pre- Rx30day compared to pre-
industrial (Annex). industrial (Annex).
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Low confidence (compared with Medium confidence (compared Likely (compared with the
the recent past (1995-2014)) with the recent past (1995- recent past (1995-2014))
Medium confidence (compared 2014)) Very likely (compared with pre-
with pre-industrial) High confidence (compared industrial)
with pre-industrial)
Eastern Australia (EAU) Lack of agreement on the Limited evidence CMIP6 models project CMIP6 models project CMIP6 models project an
evidence of trends (Donat et inconsistent changes in the inconsistent changes in the increase in the intensity and
al., 2016a; Alexander and region (Li et al., 2020a) region (Li et al., 2020a) frequency of heavy
Arblaster, 2017; Evans et al., precipitation (Li et al., 2020;
Do Not Cite, Quote or Distribute 11-179 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
2017; Dey et al., 2019b; Dunn Annex). Median increase of
et al., 2020; Sun et al., 2020) more than 10% in the 50-year
Rx1day and Rx5day events
compared to the 1°C warming
level (Li et al., 2020a) and more
than 10% in annual Rx1day and
Rx5day and 8% in annual
Rx30day compared to pre-
industrial (Annex).
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Low confidence (compared with Low confidence (compared with High confidence (compared
the recent past (1995-2014)) the recent past (1995-2014)) with the recent past (1995-
Low confidence (compared with Medium confidence (compared 2014))
pre-industrial) with pre-industrial) Likely (compared with pre-
industrial)
Southern Australia (SAU) Limited evidence (Donat et Limited evidence CMIP6 models project CMIP6 models project CMIP6 models project an
al., 2016a; Alexander and inconsistent changes in the inconsistent changes in the increase in the intensity and
Arblaster, 2017; Evans et al., region (Li et al., 2020a) region (Li et al., 2020a) frequency of heavy
2017; Dey et al., 2019b; Dunn precipitation (Li et al., 2020;
et al., 2020; Sun et al., 2020) Annex). Median increase of
more than 10% in the 50-year
Rx1day and Rx5day events
compared to the 1°C warming
level (Li et al., 2020a) and more
than 8% in annual Rx1day and
Rx5day and 4% in annual
Rx30day compared to pre-
industrial (Annex).
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Low confidence (compared with Low confidence (compared with High confidence (compared
the recent past (1995-2014)) the recent past (1995-2014)) with the recent past (1995-
Low confidence (compared with Medium confidence (compared 2014))
pre-industrial) with pre-industrial) Likely (compared with pre-
industrial)
New Zealand (NZ) Lack of agreement on the Limited evidence (Rosier et CMIP6 models project CMIP6 models project CMIP6 models project an
evidence of trends (Donat et al., 2016) inconsistent changes in the inconsistent changes in the increase in the intensity and
al., 2016a; Dunn et al., 2020; region (Li et al., 2020a) region (Li et al., 2020a) frequency of heavy
MfE and Stats NZ, 2020) precipitation (Li et al., 2020;
Annex). Median increase of
more than 15% in the 50-year
Rx1day and Rx5day events
compared to the 1°C warming
level (Li et al., 2020a) and
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Final Government Distribution Chapter11 IPCC AR6 WGI
more than 15% in annual
Rx1day and Rx5day and 10%
in annual Rx30day compared
to pre-industrial (Annex).
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Low confidence (compared Low confidence (compared High confidence (compared
with the recent past (1995- with the recent past (1995- with the recent past (1995-
2014)) 2014)) 2014))
Low confidence (compared Medium confidence Likely (compared with pre-
with pre-industrial) (compared with pre- industrial)
industrial)
1
2 [END TABLE 11.11 HERE]
3
4
5 [START TABLE 11.12 HERE]
6
7 Table 11.12: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for meteorological droughts (MET),
8 agricultural and ecological droughts (AGR/ECOL), and hydrological droughts (HYDR) in Australasia, subdivided by AR6 regions. See Sections 11.9.1 and 11.9.4 for
9 details.
Region and Observed trends Human contribution Projections
drought type +1.5 °C +2 °C +4 °C
Northern MET Medium confidence: Decrease in Low confidence in Low confidence: Increases or non- Low confidence: Increases or non- Low confidence: Increases or non-
Australia the frequency and intensity of attribution (Delworth robust changes in meteorological robust changes in meteorological robust changes in meteorological
(NAU) meteorological droughts (Gallant and Zeng, 2014; droughts (Alexander and Arblaster, droughts (Alexander and Arblaster, droughts (Alexander and Arblaster,
et al., 2013; Delworth and Zeng, Knutson and Zeng, 2017; Kirono et al., 2020; Spinoni 2017; Kirono et al., 2020; Spinoni 2017; Grose et al., 2020; Kirono et
2014; Alexander and Arblaster, 2018; Dey et al., et al., 2020)(Chapter 11 et al., 2020)(Chapter 11 al., 2020; Spinoni et al., 2020;
2017; Knutson and Zeng, 2018; 2019a). Supplementary Material (11.SM)). Supplementary Material (11.SM)). Ukkola et al., 2020)(Chapter 11
Dey et al., 2019a; Dunn et al., Supplementary Material (11.SM)).
2020) Model disagreement in SPI Large intermodel spread in changes
projections (Spinoni et al., 2020) in SPI in CMIP5 projections Large intermodel spread in changes
(Kirono et al., 2020) in SPI in CMIP5 projectons, but
Increase in CDD-based drought in slight drying for median (Kirono et
CMIP5, but generally not Model disagreement in SPI al., 2020)
significant (Alexander and projections (Spinoni et al., 2020)
Arblaster, 2017) Model disagreement in SPI
Increase in CDD-based drought in projections (Spinoni et al., 2020)
Slight increase in CDD-based CMIP5, but generally not
drought in CMIP6 (Chapter 11 significant (Alexander and Increase in CDD-based drought in
Supplementary Material (11.SM)) Arblaster, 2017) CMIP5, but generally not significant
(Alexander and Arblaster, 2017)
Slight increase in CDD-based
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Final Government Distribution Chapter11 IPCC AR6 WGI
drought CMIP6 (Chapter 11 Increase in CDD-based drought in
Supplementary Material (11.SM)) CMIP6 (Grose et al., 2020)(Chapter
11 Supplementary Material (11.SM))
Inconsistent trends in mean
precipitation in CORDEX RCMs,
but drying trend on annual scale at
northern tip of region (Evans et al.,
2020)
AGR Medium confidence: Decrease in Low confidence Low confidence: Increase or non- Low confidence: Increase or non- Low confidence: Increase or non-
ECOL agricultural and ecological Limited evidence robust (Naumann et al., 2018; Xu robust (Naumann et al., 2018; Xu robust, with higher increases in
drought et al., 2019a; Cook et al., 2020; et al., 2019a; Cook et al., 2020; SPEI-PM but non-robust changes in
Lack of studies Kirono et al., 2020).(Chapter 11 Kirono et al., 2020).(Chapter 11 CMIP6 soil moisture (Naumann et
Decrease in frequency (but not although (Lewis et al., Supplementary Material (11.SM)) Supplementary Material (11.SM)) al., 2018; Cook et al., 2020; Kirono
intensity) of soil moisture-based 2019b) supported an et al., 2020; Vicente-Serrano et al.,
droughts (Gallant et al., 2013). anthropogenic Cook et al. (2020): non-robust 2020a)(Chapter 11 Supplementary
Inconsistent signals in changes in attribution of 2018 Cook et al. (2020): non-robust changes in surface and column soil Material (11.SM)).
water-balance (Greve et al., 2014; drought associated changes in surface and column soil moisture in both summer and winter
Padrón et al., 2019). with more extreme moisture in both summer and winter half years (CMIP6 projections) Cook et al. (2020): non-robust
Decrease in agricultural and temperatures that half years (CMIP6 projections) changes in surface and column soil
ecological drought based on exacerbated AED and Kirono et al. (2020): Standardized moisture in both summer and winter
SPEI-PM from 1950-2009 ET, and depleting soil soil moisture index based on half years (CMIP6 projections)
(Beguería et al., 2014; Spinoni et moisture. surface soil moisture: drying trend
al., 2019) and PDSI_PM (Dai and for median in CMIP5 but large Kirono et al. (2020): Standardized
Zhao, 2017) intermodal spread soil moisture index based on surface
soil moisture : drying trend for
median in CMIP5, but larger inter-
model spread
HYDR Low confidence because of lack Low confidence Low confidence: Limited Low confidence: Limited evidence Low confidence: Non-robust
of data and studies Limited evidence evidence. One study shows lack of and generally non-robust change in changes or high model
because of lack of data signal (Touma et al., 2015) two studies (Touma et al., 2015; disagreement (Giuntoli et al., 2015;
and studies Cook et al., 2020) Touma et al., 2015; Cook et al.,
2020)
Central MET Medium confidence: decrease in Low confidence in Low confidence: Inconsistent or Low confidence: Inconsistent or Low confidence: Inconsistent or
Australia the frequency/intensity of attribution (Delworth non-robust changes in non-robust changes in non-robust changes in
(CAU) droughts (Gallant et al., 2013; and Zeng, 2014; meteorological droughts (Alexander meteorological droughts (Alexander meteorological droughts (Alexander
Beguería et al., 2014; Delworth Knutson and Zeng, and Arblaster, 2017; Kirono et al., and Arblaster, 2017; Kirono et al., and Arblaster, 2017; Grose et al.,
and Zeng, 2014; Greve et al., 2018). 2020; Spinoni et al., 2020)(Chapter 2020; Spinoni et al., 2020)(Chapter 2020; Kirono et al., 2020; Spinoni et
2014; Alexander and Arblaster, 11 Supplementary Material 11 Supplementary Material al., 2020; Ukkola et al., 2020)
2017; Knutson and Zeng, 2018). (11.SM)). (11.SM)). (Chapter 11 Supplementary Material
(11.SM)).
Tendency to increasing SPI-based Tendency to increasing SPI-based
drought in CMIP6, but to drought in CMIP6, but to Tendency to increasing SPI-based
decreasing SPI-based drought in decreasing SPI-based drought in drought in CMIP6, but to decreasing
CORDEX (Spinoni et al., 2020) CORDEX (Spinoni et al., 2020) SPI-based drought in CORDEX
(Spinoni et al., 2020)
Kirono et al. (2020): CMIP6 models
Do Not Cite, Quote or Distribute 11-182 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
project increased in SPI in much of Kirono et al. (2020): CMIP6 models
region for 2006-2100 under RCP8.5 project increased in SPI in much of
region for 2006-2100 under RCP8.5
AGR Low confidence: Inconsistent Low confidence Low confidence: Inconsistent Low confidence: Inconsistent Medium confidence: Increased
ECOL changes in frequency/intensity of because of lack of changes both in soil moisture and changes both in soil moisture and drying for some metrics or part of
droughts (Gallant et al., 2013; studies SPEI-PM (Naumann et al., 2018; SPEI-PM (Naumann et al., 2018; domain for soil moisture and SPEI-
Beguería et al., 2014; Delworth Xu et al., 2019a; Cook et al., 2020; Xu et al., 2019a; Cook et al., 2020; PM with stronger changes for SPEI-
and Zeng, 2014; Greve et al., Kirono et al., 2020)(Chapter 11 Kirono et al., 2020)(Chapter 11 PM (Naumann et al., 2018; Cook et
2014; Dai and Zhao, 2017; Supplementary Material (11.SM)) Supplementary Material (11.SM)). al., 2020; Kirono et al., 2020;
Knutson and Zeng, 2018; Padrón Vicente-Serrano et al.,
et al., 2019; Spinoni et al., 2019) 2020a)(Chapter 11 Supplementary
. Material (11.SM))
HYDR Low confidence because of lack Low confidence Low confidence: Limited Low confidence: Limited evidence Low confidence: Non-robust
of data and studies Limited evidence, evidence. One study shows lack of and generally non-robust change in changes or high model
because of lack of signal (Touma et al., 2015) two studies (Touma et al., 2015; disagreement (Giuntoli et al., 2015;
studies Cook et al., 2020) Touma et al., 2015; Cook et al.,
2020)
Eastern MET Low confidence: Inconsistent Low confidence in Low confidence: Increase in Medium confidence: Increases in Medium confidence: Increases in
Australia trends (Gallant et al., 2013; attribution (Delworth meteorological droughs based on meteorological droughts (Alexander meteorological droughts (Alexander
(EAU) Delworth and Zeng, 2014; and Zeng, 2014; King CDD (Chapter 11 Supplementary and Arblaster, 2017; Kirono et al., and Arblaster, 2017; Grose et al.,
Alexander and Arblaster, 2017; et al., 2014; Knutson Material (11.SM)) and SPI (Kirono 2020; Spinoni et al., 2020)(Chapter 2020; Kirono et al., 2020; Spinoni et
Knutson and Zeng, 2018; Spinoni and Zeng, 2018) et al., 2020), but weak signals and 11 Supplementary Material al., 2020; Ukkola et al.,
et al., 2019) lack of other studies at this GWL. (11.SM)) . 2020)(Chapter 11 Supplementary
Material (11.SM)).
Gallant et al. (2013): Inconsistent
trends, wetting on average in
MDB
Delworth and Zeng (2014): no
trend
Knutson and Zeng (2018): no
trend
Alexander and Arblaster (2017);
Dunn et al. (2020): no trends in
CDD
Spinoni et al. (2019): Inconsistent
trends, some increased severity in
part of the region
AGR Low confidence: Inconsistent Low confidence Low confidence: Inconsistent Medium confidence: Increase in High confidence: Increased drying
ECOL trends (Gallant et al., 2013; because of lack of changes in soil moisture and SPEI- drought based on soil moisture and for some metrics or part of domain
Beguería et al., 2014; Greve et studies although PM, but tendency to increase SPEI-PM, but partly inconsistent for soil moisture and SPEI-PM with
al., 2014; Dai and Zhao, 2017; enhanced AED driven (Naumann et al., 2018; Xu et al.,
changes for some studies (Naumann stronger changes for SPEI-PM
Spinoni et al., 2019; Padrón et al., by extreme 2019a; Cook et al., 2020; Kirono et
2020) temperatures increased al., 2020)(Chapter 11 et al., 2018; Xu et al., 2019a; Cook (Naumann et al., 2018; Cook et al.,
the severity of the Supplementary Material (11.SM)) et al., 2020; Kirono et al., 2020; Kirono et al., 2020; Vicente-
2019 drought (van 2020)(Chapter 11 Supplementary Serrano et al., 2020a)(Chapter 11
Oldenborgh et al., Material (11.SM)) Supplementary Material (11.SM))
2021)
Do Not Cite, Quote or Distribute 11-183 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Lack of studies Low confidence: Non-robust
evidence because of lack of data Limited evidence, evidence. One study shows lack of and generally non-robust change in changes or high model
and studies (Zhang et al., 2016d) because of lack of signal (Touma et al., 2015) two studies (Touma et al., 2015; disagreement (Giuntoli et al., 2015;
studies Cook et al., 2020) Touma et al., 2015; Cook et al.,
2020)
Southern MET Low confidence: Mixed signal Low confidence: Medium confidence: Increase Medium confidence: Increases in Medium confidence: Increases in
Australia depending on subregion, index Mixed signal in overall in meteorological droughts meteorological droughts (Alexander meteorological droughts (Alexander
(SAU) and season (Gallant et al., 2013; observations. based on CDD (Chapter 11 and Arblaster, 2017; Kirono et al., and Arblaster, 2017; Grose et al.,
Delworth and Zeng, 2014; Supplementary Material (11.SM)) 2020; Spinoni et al., 2020)(Chapter 2020; Kirono et al., 2020; Spinoni et
Alexander and Arblaster, 2017; Increase in the and SPI (Kirono et al., 2020); but 11 Supplementary Material al., 2020; Ukkola et al.,
Spinoni et al., 2019; Dunn et al., frequency/intensity of weak signals and lack of other (11.SM)). 2020)(Chapter 11 Supplementary
2020; Rauniyar and Power, meteorological studies at this GWL. Material (11.SM)).
2020)(Dai and Zhao, 2017). droughts can be
attributed to
Gallant et al. (2013): Wetting in anthropogenic forcing
eastern part, drying in eastern part (greenhouse gases,
ozone and aerosols)
Rauniyar and Power (2020): (Delworth and Zeng,
Recovery from Millenium 2014; Karoly et al.,
drought 2016; Knutson and
Zeng, 2018) (Cai et
Delworth and Zeng (2014): Only al., 2014b).
drying in the western part, not in
the eastern part
Alexander and Arblaster (2017);
Dunn et al. (2020): Overall
decreasing CDD trends
Spinoni et al. (2019): Decreasing
droughts in most of domain
AGR Medium confidence: Increase. Low confidence: Medium confidence: Increase in Medium confidence: Increase in High confidence: Increased drying
ECOL Dominant increasing drying Limited evidence, soil moisture and SPEI-PM, but drought based on soil moisture and for some metrics or part of domain
signal but some inconsistent Enhanced AED driven partly inconsistent changes for SPEI-PM, but partly inconsistent for soil moisture and SPEI-PM with
trends depending on subregion by extreme
some studies (Naumann et al., changes for some studies (Naumann stronger changes for SPEI-PM
and index; strongest drying trend temperatures increased
in Western SAU. (Gallant et al., the severity of the 2018; Xu et al., 2019a; Kirono et et al., 2018; Xu et al., 2019a; Cook (Naumann et al., 2018; Cook et al.,
2013; Beguería et al., 2014; 2019 drought (van al., 2020)(Chapter 11 et al., 2020; Kirono et al., 2020; Kirono et al., 2020; Vicente-
Greve et al., 2018; Spinoni et al., Oldenborgh et al., Supplementary Material (11.SM)). 2020)(Chapter 11 Supplementary Serrano et al., 2020a)(Chapter 11
2019; Padrón et al., 2020). 2021) Material (11.SM)) Supplementary Material (11.SM))
Do Not Cite, Quote or Distribute 11-184 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
HYDR Medium confidence: Increasing Low confidence : Low confidence: Limited Medium confidence: Increase in Medium confidence: Increase in
drying signal in the southeast Limited evidence evidence. One study shows lack of drought, but some Inconsistent and drought, but some inconsistent
and particularly the southwest. because of lack of signal (Touma et al., 2015) non-robust change including changes depending on season or
Some dependence on time studies (Cai and subregional/seasonal differences study (Giuntoli et al., 2015; Touma
frame in available studies Cowan, 2008) (Touma et al., 2015; Zheng et al., et al., 2015; Cook et al., 2020)
(Gudmundsson et al., 2019, 2019; Cook et al., 2020)
2021)(Zhang et al., 2016d)
New MET Low confidence: Inconsistent Low confidence in Low confidence: Lack of studies Low confidence: Inconsistent Low confidence: Inconsistent
Zealand changes (Caloiero, 2015; Spinoni attribution of trends and lack of signal for CDD in changes, but increase in Northern changes, but increase in Northern
(NZ) et al., 2015; Knutson and Zeng, (Harrington et al., CMIP6 (Chapter 11 Supplementary Island Island (MfE, 2018; MfE and Stats
2018) 2014, 2016; Knutson (MfE, 2018; MfE and Stats NZ, NZ, 2020; Spinoni et al.,
Material (11.SM))
and Zeng, 2018). 2020; Spinoni et al., 2020).(Chapter 2020).(Chapter 11 Supplementary
11 Supplementary Material Material (11.SM))
(11.SM))
AGR Low confidence: Inconsistent Low confidence: Low confidence: Lack of studies Low confidence: Inconsistent Low confidence: Inconsistent
ECOL trends. Increase in drying in part Limited evidence and lack of signal for soil changes, but increase in Northern changes, but increase in Northern
of the country based on soil because of lack of moisture in CMIP6 (Chapter 11 Island (MfE, 2018; MfE and Stats Island (MfE, 2018; MfE and Stats
mosture and SPEI-PM (Beguería studies Supplementary Material (11.SM)) NZ, 2020; Spinoni et al., 2020). NZ, 2020; Spinoni et al., 2020).
et al., 2014; Spinoni et al., 2019;
MfE and Stats NZ, 2020);
decrease in PDSI-PM (Dai and
Zhao, 2017)
HYDR Low confidence: Lack of data Low confidence: Lack Low confidence: Lack of studies Low confidence: Lack of studies Low confidence: Lack of studies
and studies of studies
1
2 [END TABLE 11.12 HERE]
3
4
5 [START TABLE 11.13 HERE]
6
7 Table 11.13: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for temperature extremes in Central and
8 South America, subdivided by AR6 regions. See Sections 11.9.1 and 11.9.2 for details.
Detection and attribution; Projections
Region Observed trends
event attribution 1.5 °C 2 °C 4 °C
All Central and South Most subregions show a likely Robust evidence of a human CMIP6 models project a CMIP6 models project a CMIP6 models project a
America increase in the intensity and contribution to the observed robust increase in the robust increase in the robust increase in the
frequency of hot extremes increase in the intensity and intensity and frequency of intensity and frequency of intensity and frequency of
and decrease in the intensity frequency of hot extremes TXx events and a robust TXx events and a robust TXx events and a robust
and frequency of cold and decrease in the intensity decrease in the intensity and decrease in the intensity and decrease in the intensity and
extremes and frequency of cold frequency of TNn events (Li frequency of TNn events (Li frequency of TNn events (Li
extremes (Hu et al., 2020; et al., 2020). Median increase et al., 2020). Median increase et al., 2020). Median increase
Seong et al., 2020) of more than 0.5°C in the 50- of more than 1°C in the 50- of more than 2.5°C in the 50-
year TXx and TNn events year TXx and TNn events year TXx and TNn events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020) level (Li et al., 2020) level (Li et al., 2020)
Do Not Cite, Quote or Distribute 11-185 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations CMIP5 and RCM simulations CMIP5 and RCM simulations
for an increase in the intensity for an increase in the intensity for an increase in the intensity
and frequency of hot and frequency of hot and frequency of hot
extremes and decrease in the extremes and decrease in the extremes and decrease in the
intensity and frequency of intensity and frequency of intensity and frequency of
cold extremes (Chou et al., cold extremes (Chou et al., cold extremes (Chou et al.,
2014a) 2014a) 2014a)
High confidence in the High confidence in a human Increase in the intensity and Increase in the intensity and Increase in the intensity and
increase in the intensity and contribution to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
frequency of hot extremes increase in the intensity and Very likely (compared with Extremely likely (compared Virtually certain (compared
and decrease in the intensity frequency of hot extremes the recent past (1995-2014)) with the recent past (1995- with the recent past (1995-
and frequency of cold and decrease in the intensity Extremely likely (compared 2014)) 2014))
extremes and frequency of cold with pre-industrial) Virtually certain (compared Virtually certain (compared
with pre-industrial) with pre-industrial)
extremes
Decrease in the intensity and
frequency of cold extremes: Decrease in the intensity and Decrease in the intensity and
Very likely (compared with frequency of cold extremes: frequency of cold extremes:
the recent past (1995-2014)) Extremely likely (compared Virtually certain (compared
Extremely likely (compared with the recent past (1995- with the recent past (1995-
with pre-industrial) 2014)) 2014))
Virtually certain (compared Virtually certain (compared
with pre-industrial) with pre-industrial)
South Central America (SCA) Increases in the intensity and Evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
frequency of hot extremes contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
and decreases in the intensity increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
and frequency of cold frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
extremes (Dunn et al. 2020; and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
Aguilar et al. 2005) and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
extremes (Wang et al. 2017, increase of more than 0.5°C in Median increase of more than Median increase of more than
Seong et al. 2020) the 50-year TXx and TNn events 0.5°C in the 50-year TXx and 2.5°C in the 50-year TXx and
compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 3.5°C in
TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5 and RCM simulations
an increase in the intensity and for an increase in the intensity for an increase in the intensity
frequency of hot extremes and and frequency of hot extremes and frequency of hot extremes
decrease in the intensity and and decrease in the intensity and decrease in the intensity
frequency of cold extremes and frequency of cold extremes and frequency of cold extremes
(Imbach et al., 2018; Angeles- (Chou et al., 2014a) (Coppola et al., 2021b;
Malaspina et al., 2018; Angeles-Malaspina et al., 2018;
Chou et al., 2014) Chou et al., 2014)
Medium confidence in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
Do Not Cite, Quote or Distribute 11-186 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
increase in the intensity and human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
frequency of hot extremes observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
and decrease in the intensity intensity and frequency of hot past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
and frequency of cold extremes and decrease in the Very likely (compared with pre- Extremely likely (compared 2014))
extremes intensity and frequency of industrial) with pre-industrial) Virtually certain (compared
cold extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with with the recent past (1995-
industrial). pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Caribbean (CAR) Significant increases in the Strong evidence of changes CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot from observations that are in increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant the direction of model frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and projected changes for the robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes future. The magnitude of and frequency of TNn events and frequency of TNn events and frequency of TNn events
(Angeles-Malaspina, projected changes increases (Annex). Median increase of (Li et al., 2020; Annex). (Li et al., 2020; Annex).
González-Cruz & Ramírez- with global warming. more than 1.5°C in annual TXx Median increase of more than Median increase of more than
Beltran; 2018; McLean et al., and TNn compared to pre- XC in the 50-year TXx and XC in the 50-year TXx and
2015; Dunn et al., 2020) industrial (Annex). TNn events compared to the TNn events compared to the
1°C warming level (Li et al., 1°C warming level (Li et al.,
Additional evidence from 2020) and more than 2°C in 2020) and more than 3.5°C in
CMIP5 and RCM simulations for annual TXx and TNn compared annual TXx and TNn compared
an increase in the intensity and to pre-industrial (Annex). to pre-industrial (Annex).
frequency of hot extremes and
decrease in the intensity and Additional evidence from Additional evidence from
frequency of cold extremes CMIP5 and RCM simulations CMIP5 and RCM simulations
(Angeles-Malaspina, González- for an increase in the intensity for an increase in the intensity
Cruz & Ramírez-Beltran; 2018; and frequency of hot extremes and frequency of hot extremes
Chou et al., 2014) and decrease in the intensity and decrease in the intensity
and frequency of cold extremes and frequency of cold extremes
(Chou et al., 2014a) (Coppola et al., 2021b;
Angeles-Malaspina, González-
Cruz & Ramírez-Beltran; 2018;
Chou et al., 2014; Hall et al.,
2013)
Likely increase in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of intensity and frequency of hot past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes extremes and decrease in the Very likely (compared with pre- Extremely likely (compared 2014))
intensity and frequency of industrial) with pre-industrial) Virtually certain (compared
cold extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
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Final Government Distribution Chapter11 IPCC AR6 WGI
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with with the recent past (1995-
industrial). pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Northwestern South America Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
(NWS) intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Dereczynski et al., 2020; and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Dunn et al., 2020) extremes (Seong et al., 2020) increase of more than 0C in the Median increase of more than Median increase of more than
50-year TXx and TNn events 0.5°C in the 50-year TXx and 2°C in the 50-year TXx and
compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 4.5°C in
TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5/CMIP3 and RCM
an increase in the intensity and for an increase in the intensity simulations for an increase in
frequency of hot extremes and and frequency of hot extremes the intensity and frequency of
decrease in the intensity and and decrease in the intensity hot extremes and decrease in
frequency of cold extremes and frequency of cold extremes the intensity and frequency of
(Chou et al., 2014a). (Chou et al., 2014a). cold extremes (López-Franca et
al., 2016; Coppola et al., 2021b;
Chou et al., 2014)
Likely increase in the High confidence in a human Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contribution to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with with the recent past (1995-
industrial). pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Northern South America Significant increases in the Evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
(NSA) intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
Do Not Cite, Quote or Distribute 11-188 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Dereczynski et al., 2020), and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Avila-Diaz et al.; 2020; extremes (Seong et al., 2020) increase of more than 0.5°C in Median increase of more than Median increase of more than
Geirinhas et al., 2018; Dunn the 50-year TXx and TNn events 1°C in the 50-year TXx and 3°C in the 50-year TXx and
et al., 2020) compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 4.5°C in
TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5/CMIP3 and RCM
an increase in the intensity and for an increase in the intensity simulations for an increase in
frequency of hot extremes and and frequency of hot extremes the intensity and frequency of
decrease in the intensity and and decrease in the intensity hot extremes and decrease in
frequency of cold extremes and frequency of cold extremes the intensity and frequency of
(Chou et al., 2014a). (Chou et al., 2014a). cold extremes (López-Franca et
al., 2016; Coppola et al., 2021b;
Chou et al., 2014)
Likely increase in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of intensity and frequency of hot past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes extremes and decrease in the Very likely (compared with pre- Extremely likely (compared 2014))
intensity and frequency of industrial) with pre-industrial) Virtually certain (compared
cold extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with with the recent past (1995-
industrial). pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
South American Monsoon Significant increases in the Evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
(SAM) intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Dereczynski et al., 2020; and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Avila-Diaz et al., 2020; extremes (Seong et al., 2020) increase of more than 0.5°C in Median increase of more than Median increase of more than
Geirinhas et al., 2018; Dunn the 50-year TXx and TNn events 1°C in the 50-year TXx and 2.5°C in the 50-year TXx and
et al., 2020) compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 4.5°C in
Do Not Cite, Quote or Distribute 11-189 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5/CMIP3 and RCM
an increase in the intensity and for an increase in the intensity simulations for an increase in
frequency of hot extremes and and frequency of hot extremes the intensity and frequency of
decrease in the intensity and and decrease in the intensity hot extremes and decrease in
frequency of cold extremes and frequency of cold extremes the intensity and frequency of
(Chou et al., 2014a). (Chou et al., 2014a). cold extremes (López-Franca et
al., 2016; Coppola et al., 2021b;
Chou et al., 2014)
Likely increase in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of intensity and frequency of hot past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes extremes and decrease in the Very likely (compared with pre- Extremely likely (compared 2014))
intensity and frequency of industrial) with pre-industrial) Virtually certain (compared
cold extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with with the recent past (1995-
industrial). pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Northeastern South America Significant increases in the Evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
(NES) intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Dereczynski et al., 2020), and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Avila-Diaz et al.; 2020; extremes (Seong et al., 2020) increase of more than 0.5°C in Median increase of more than Median increase of more than
Geirinhas et al., 2018; Dunn the 50-year TXx and TNn events 0.5°C in the 50-year TXx and 2.5°C in the 50-year TXx and
et al., 2020) compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 4°C in
TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5/CMIP3 and RCM
an increase in the intensity and for an increase in the intensity simulations for an increase in
frequency of hot extremes and and frequency of hot extremes the intensity and frequency of
decrease in the intensity and and decrease in the intensity hot extremes and decrease in
frequency of cold extremes and frequency of cold extremes the intensity and frequency of
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Final Government Distribution Chapter11 IPCC AR6 WGI
(Chou et al., 2014a). (Chou et al., 2014a). cold extremes (López-Franca et
al., 2016; Coppola et al., 2021b;
Chou et al., 2014)
Likely increase in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of intensity and frequency of hot past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes extremes and decrease in the Very likely (compared with pre- Extremely likely (compared 2014))
intensity and frequency of industrial) with pre-industrial) Virtually certain (compared
cold extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with with the recent past (1995-
industrial). pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Southwestern South America Significant increases in the Evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
(SWS) intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Dereczynski et al., 2020; and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Olmo et al., 2020; Dunn et extremes (Seong et al., 2020) increase of more than 0.5°C in Median increase of more than Median increase of more than
al., 2020) the 50-year TXx and TNn events 1°C in the 50-year TXx and 3°C in the 50-year TXx and
compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1.5°C in annual TXx and 2020) and more than 2.5°C in 2020) and more than 4.5°C in
TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5/CMIP3 and RCM
an increase in the intensity and for an increase in the intensity simulations for an increase in
frequency of hot extremes and and frequency of hot extremes the intensity and frequency of
decrease in the intensity and and decrease in the intensity hot extremes and decrease in
frequency of cold extremes and frequency of cold extremes the intensity and frequency of
(Chou et al., 2014a). (Chou et al., 2014a). cold extremes (López-Franca et
al., 2016; Coppola et al., 2021b;
Chou et al., 2014)
Likely increase in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of intensity and frequency of hot past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes extremes and decrease in the Very likely (compared with pre- Extremely likely (compared 2014))
intensity and frequency of industrial) with pre-industrial) Virtually certain (compared
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Final Government Distribution Chapter11 IPCC AR6 WGI
cold extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with with the recent past (1995-
industrial). pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Southeastern South America Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
(SES) intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Dereczynski et al., 2020; and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Avila-Diaz et al., 2020; extremes (Seong et al., 2020; increase of more than 0.5°C in Median increase of more than Median increase of more than
Geirinhas et al., 2018; Wang et al., 2017) the 50-year TXx and TNn events 1°C in the 50-year TXx and 3.5°C in the 50-year TXx and
Rusticucci et al., 2017; Dunn compared to the 1°C warming TNn events compared to the TNn events compared to the
et al., 2020) level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1°C in annual TXx and TNn 2020) and more than 1.5°C in 2020) and more than 3.5°C in
compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5/CMIP3 and RCM
an increase in the intensity and for an increase in the intensity simulations for an increase in
frequency of hot extremes and and frequency of hot extremes the intensity and frequency of
decrease in the intensity and and decrease in the intensity hot extremes and decrease in
frequency of cold extremes and frequency of cold extremes the intensity and frequency of
(Chou et al., 2014a). (Chou et al., 2014a). cold extremes (López-Franca et
al., 2016; Coppola et al., 2021b;
Chou et al., 2014).
High confidence in the High confidence in a human Increase in the intensity and Increase in the intensity and Increase in the intensity and
increase in the intensity and contribution to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
frequency of hot extremes increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
and decrease in the intensity frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
and frequency of cold and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
extremes and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with with the recent past (1995-
industrial). pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
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Final Government Distribution Chapter11 IPCC AR6 WGI
Southern South America Inconsistent trends and CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
(SSA) insufficient data increase in the intensity and increase in the intensity and increase in the intensity and
(Dereczynski et al., 2020; frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
Ceccherini et al., 2016; robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
(1980-2014) Dunn et al., and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
2020) et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
increase of more than 0.5°C in Median increase of more than Median increase of more than
the 50-year TXx and TNn events 1°C in the 50-year TXx and 2.5°C in the 50-year TXx and
compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1.5°C in annual TXx and 2020) and more than 2°C in 2020) and more than 4.5°C in
TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5/CMIP3 and RCM
an increase in the intensity and for an increase in the intensity simulations for an increase in
frequency of hot extremes and and frequency of hot extremes the intensity and frequency of
decrease in the intensity and and decrease in the intensity hot extremes and decrease in
frequency of cold extremes and frequency of cold extremes the intensity and frequency of
(Chou et al., 2014a). (Chou et al., 2014a). cold extremes (López-Franca et
al., 2016; Coppola et al., 2021b;
Chou et al., 2014).
Low confidence Low confidence Increase in the intensity and Increase in the intensity and Increase in the intensity and
frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
Likely (compared with the recent Very likely (compared with the Virtually certain (compared
past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
Very likely (compared with pre- Extremely likely (compared 2014))
industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with with the recent past (1995-
industrial). pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
1
2 [END TABLE 11.13 HERE]
3
4
5 [START TABLE 11.14 HERE]
6
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Final Government Distribution Chapter11 IPCC AR6 WGI
1 Table 11.14: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for heavy precipitation in Central and South
2 America, subdivided by AR6 regions. See Sections 11.9.1 and 11.9.3 for details.
Projections
Detection and attribution;
Region Observed trends
event attribution 1.5 °C 2 °C 4 °C
All Central and South Insufficient data to assess Limited evidence CMIP6 models project an CMIP6 models project an CMIP6 models project a
America trends increase in the intensity and increase in the intensity and robust increase in the
frequency of heavy frequency of heavy intensity and frequency of
precipitation (Li et al., precipitation (Li et al., heavy precipitation (Li et al.,
2020a). Median increase of 2020a). Median increase of 2020a). Median increase of
more than 0% in the 50-year more than 4% in the 50-year more than 10% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) level (Li et al., 2020a) level (Li et al., 2020a)
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations CMIP5 and RCM simulations CMIP5 and RCM simulations
for an increase in the intensity for an increase in the intensity for an increase in the intensity
of heavy precipitation (Chou of heavy precipitation (Chou of heavy precipitation (Chou
et al., 2014a) et al., 2014a) et al., 2014a)
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Medium confidence High confidence (compared Very likely (compared with
(compared with the recent with the recent past (1995- the recent past (1995-2014))
past (1995-2014)) 2014)) Extremely likely (compared
High confidence (compared Likely (compared with pre- with pre-industrial)
with pre-industrial) industrial)
South Central America (SCA) Insufficient data coverage and Limited evidence CMIP6 models, CMIP5 CMIP6 models, CMIP5 CMIP6 models, CMIP5
trends in available data are models, and RCMs project models, and RCMs project models, and RCMs project
generally not significant (Sun inconsistent changes in the inconsistent changes in the inconsistent changes in the
et al., 2020; Dunn et al., region (Li et al., 2020; region (Li et al., 2020; Chou region (Li et al., 2020; Chou
2020; Stephenson et al., Imbach et al., 2018; Chou et et al., 2014). et al., 2014; Coppola et al.,
2014) al., 2014). 2021b; Kusunoki et al., 2019;
Nakaegawa et al., 2013)
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Low confidence (compared Low confidence (compared Low confidence (compared
with the recent past (1995- with the recent past (1995- with the recent past (1995-
2014)) 2014)) 2014))
Low confidence (compared Low confidence (compared Medium confidence
with pre-industrial) with pre-industrial) (compared with pre-
industrial)
Caribbean (CAR) Insufficient data and a lack of Evidence of a human CMIP6 models, CMIP5 CMIP6 models, CMIP5 CMIP6 models, CMIP5
agreement on the evidence of contribution for some events models, and RCMs project models, and RCMs project models, and RCMs project
trends (Sun et al., 2020; Dunn (Patricola and Wehner, 2018), inconsistent changes in the inconsistent changes in the inconsistent changes in the
et al., 2020; McLean et al., but cannot be generalized region (Li et al., 2020; Chou region (Li et al., 2020; Chou region (Li et al., 2020;
2015; Stephenson et al., et al., 2014) et al., 2014) Coppola et al., 2021b; Chou
2014) et al., 2014; Nakaegawa et al.,
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Final Government Distribution Chapter11 IPCC AR6 WGI
2013; Hall et al., 2013)
Low confidence Low confidence. Low confidence Low confidence Low confidence
Northwestern South America Insufficient data coverage and Disagreement among studies CMIP6 models, CMIP5 CMIP6 models, CMIP5 CMIP6 models, CMIP5
(NWS) trends in available data are (Li et al., 2019; Otto et al., models, and RCMs project models, and RCMs project models, and RCMs project
generally not significant (Sun 2018a) inconsistent changes in the inconsistent changes in the inconsistent changes in the
et al., 2020; Dunn et al., region (Li et al., 2020; Chou region (Li et al., 2020; Chou region (Li et al., 2020; Chou
2020; Dereczynski et al., et al., 2014) et al., 2014) et al., 2014)
2020)
Low confidence Low confidence Low confidence Low confidence Low confidence
Northern South America Insufficient data coverage and Evidence of a human Conflicting projections by the CMIP6 models project an CMIP6 models project an
(NSA) trends in available data are contribution for some events CMIP6 multi-model increase in the intensity and increase in the intensity and
generally not significant (Sun (Li et al., 2019d), but cannot ensemble and limited RCM frequency of heavy frequency of heavy
et al., 2020; Dunn et al., be generalized simulations; more weight is precipitation (Li et al., 2020; precipitation (Li et al., 2020;
2020; Dereczynski et al., given to the CMIP6 results. Annex). Median increase of Annex). Median increase of
2020; Avila-Diaz et al., 2020) more than 4% in the 50-year more than 15% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and level (Li et al., 2020a) and
more than 4% in annual more than 10% in annual
Rx1day and Rx5day and 0% Rx1day and Rx5day and 0%
in annual Rx30day compared in annual Rx30day compared
to pre-industrial (Annex). to pre-industrial (Annex).
Conflicting projections by the Conflicting projections by the
CMIP6 multi-model CMIP6 multi-model
ensemble and limited RCM ensemble and limited RCM
simulations; more weight is simulations; more weight is
given to the CMIP6 results. given to the CMIP6 results.
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Low confidence (compared Medium confidence Medium confidence
with the recent past (1995- (compared with the recent (compared with the recent
2014)) past (1995-2014)) past (1995-2014))
Medium confidence Medium confidence Medium confidence
(compared with pre- (compared with pre- (compared with pre-
industrial) industrial) industrial)
South American Monsoon Insufficient data coverage and Evidence of a human CMIP6 models, CMIP5 CMIP6 models project an CMIP6 models project an
(SAM) trends in available data are contribution for some events models, and RCMs project increase in the intensity and increase in the intensity and
generally not significant (Sun (Li et al., 2019d), but cannot inconsistent changes in the frequency of heavy frequency of heavy
et al., 2020; Dunn et al., be generalized region (Li et al., 2020; Chou precipitation (Li et al., 2020; precipitation (Li et al., 2020;
2020; Dereczynski et al., et al., 2014) Annex). Median increase of Annex). Median increase of
2020; Avila-Diaz et al., 2020) more than 2% in the 50-year more than 10% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and level (Li et al., 2020a) and
more than 6% in annual more than 10% in annual
Rx1day and Rx5day and 2% Rx1day and Rx5day and 4%
in annual Rx30day compared in annual Rx30day compared
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Final Government Distribution Chapter11 IPCC AR6 WGI
to pre-industrial (Annex). to pre-industrial (Annex).
Conflicting projections by the Conflicting projections by the
CMIP6 multi-model CMIP6 multi-model
ensemble and limited RCM ensemble and limited RCM
simulations; more weight is simulations; more weight is
given to the CMIP6 results. given to the CMIP6 results.
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Low confidence (compared Medium confidence Medium confidence
with the recent past (1995- (compared with the recent (compared with the recent
2014)) past (1995-2014)) past (1995-2014))
Medium confidence Medium confidence Medium confidence
(compared with pre- (compared with pre- (compared with pre-
industrial) industrial) industrial)
Northeastern South America Insufficient data coverage and Evidence of a human CMIP6 models, CMIP5 CMIP6 models project an CMIP6 models project an
(NES) trends in available data are contribution for some events models, and RCMs project increase in the intensity and increase in the intensity and
generally not significant (Sun (Li et al., 2019d), but cannot inconsistent changes in the frequency of heavy frequency of heavy
et al., 2020; Dunn et al., be generalized region (Li et al., 2020; Chou precipitation (Li et al., 2020; precipitation (Li et al., 2020;
2020; Dereczynski et al., et al., 2014) Annex). Median increase of Annex). Median increase of
2020; Avila-Diaz et al., 2020) more than 4% in the 50-year more than 15% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and level (Li et al., 2020a) and
more than 8% in annual more than 20% in annual
Rx1day and Rx5day and 4% Rx1day and Rx5day and 10%
in annual Rx30day compared in annual Rx30day compared
to pre-industrial (Annex). to pre-industrial (Annex).
Conflicting projections by the Conflicting projections by the
CMIP6 multi-model CMIP6 multi-model
ensemble and limited RCM ensemble and limited RCM
simulations; more weight is simulations; more weight is
given to the CMIP6 results. given to the CMIP6 results.
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Low confidence (compared Medium confidence Medium confidence
with the recent past (1995- (compared with the recent (compared with the recent
2014)) past (1995-2014)) past (1995-2014))
Medium confidence Medium confidence Medium confidence
(compared with pre- (compared with pre- (compared with pre-
industrial) industrial) industrial)
Southwestern South America Insufficient data coverage and Evidence of a human CMIP6 models, CMIP5 CMIP6 models, CMIP5 CMIP6 models, CMIP5
(SWS) trends in available data are contribution for some events models, and RCMs project models, and RCMs project models, and RCMs project
generally not significant (Sun (Li et al., 2019d), but cannot inconsistent changes in the inconsistent changes in the inconsistent changes in the
et al., 2020; Dunn et al., be generalized region (Li et al., 2020; Chou region (Li et al., 2020; Chou region (Li et al., 2020; Chou
2020; Dereczynski et al., et al., 2014) et al., 2014) et al., 2014)
2020; Olmo et al., 2020)
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Final Government Distribution Chapter11 IPCC AR6 WGI
Low confidence Low confidence Low confidence Low confidence Low confidence
Southeastern South America Significant intensification of Evidence of a human CMIP6 models, CMIP5 CMIP6 models project an CMIP6 models project a
(SES) heavy precipitation Dunn et contribution for some events models, and RCMs project increase in the intensity and robust increase in the
al., 2020; Dereczynski et al., (Li et al., 2019d), but cannot inconsistent changes in the frequency of heavy intensity and frequency of
2020; Olmo et al., 2020; be generalized region (Li et al., 2020; Chou precipitation (Li et al., 2020; heavy precipitation (Li et al.,
Avila-Diaz et al. (2020) et al., 2014) Annex). Median increase of 2020; Annex). Median
more than 4% in the 50-year increase of more than 8% in
Rx1day and Rx5day events the 50-year Rx1day and
compared to the 1°C warming Rx5day events compared to
level (Li et al., 2020a) and the 1°C warming level (Li et
more than 8% in annual al., 2020a) and more than
Rx1day and Rx5day and 6% 20% in annual Rx1day and
in annual Rx30day compared Rx5day and 15% in annual
to pre-industrial (Annex). Rx30day compared to pre-
industrial (Annex).
Additional evidence from
CMIP5 and RCM simulations Additional evidence from
for an increase in the intensity CMIP5 and RCM simulations
of heavy precipitation (Chou for an increase in the intensity
et al., 2014a) of heavy precipitation (Chou
et al., 2014a)
High confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
inintensification of heavy precipitation: precipitation: precipitation:
precipitation Low confidence (compared Medium confidence Likely (compared with the
with the recent past (1995- (compared with the recent recent past (1995-2014))
2014)) past (1995-2014)) Likely (compared with pre-
Medium confidence High confidence (compared industrial)
(compared with pre- with pre-industrial)
industrial)
Southern South America Insufficient data coverage and Evidence of a human CMIP6 models, CMIP5 CMIP6 models project an CMIP6 models project a
(SSA) trends are generally not contribution for some events models, and RCMs project increase in the intensity and robust increase in the
significant (Sun et al., 2020; (Li et al., 2019d), but cannot inconsistent changes in the frequency of heavy intensity and frequency of
Dunn et al., 2020; be generalized region (Li et al., 2020; Chou precipitation (Li et al., 2020; heavy precipitation (Li et al.,
Dereczynski et al., 2020) et al., 2014) Annex). Median increase of 2020; Annex). Median
more than 4% in the 50-year increase of more than 15% in
Rx1day and Rx5day events the 50-year Rx1day and
compared to the 1°C warming Rx5day events compared to
level (Li et al., 2020a) and the 1°C warming level (Li et
more than 2% in annual al., 2020a) and more than 8%
Rx1day and Rx5day and 0% in annual Rx1day and Rx5day
in annual Rx30day compared and 2% in annual Rx30day
to pre-industrial (Annex). compared to pre-industrial
(Annex).
Additional evidence from
CMIP5 and RCM simulations Additional evidence from
for an increase in the intensity CMIP5 and RCM simulations
of heavy precipitation (Chou for an increase in the intensity
et al., 2014a) of heavy precipitation (Chou
et al., 2014a)
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Final Government Distribution Chapter11 IPCC AR6 WGI
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Low confidence (compared Medium confidence Likely (compared with the
with the recent past (1995- (compared with the recent recent past (1995-2014))
2014)) past (1995-2014)) Very likely (compared with
Medium confidence High confidence (compared pre-industrial)
(compared with pre- with pre-industrial)
industrial)
1
2 [END TABLE 11.14 HERE]
3
4
5 [START TABLE 11.15 HERE]
6
7 Table 11.15: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for meteorological droughts (MET),
8 agricultural and ecological droughts (AGR/ECOL), and hydrological droughts (HYDR) in Central and South America, subdivided by AR6 regions. See Sections 11.9.1
9 and 11.9.4 for details.
Region Observed trends Projections
Human contribution +1.5 °C +2 °C +4 °C
South MET Low confidence: Mixed Low confidence: Low confidence: Limited Medium confidence: Increase in High confidence: Increase in drought
Central signal. Dominant decrease in Limited evidence. evidence. Available evidence drought severity (Chou et al., severity (Nakaegawa et al., 2013;
America drought duration but mixed suggests increase in drought 2014a; Imbach et al., 2018; Xu et Chou et al., 2014a; Touma et al., 2015;
(SCA) trends between subregions severity (Chapter 11 Supplementary al., 2019a; Spinoni et al., Corrales-Suastegui et al., 2019;
(Aguilar et al., 2005; Spinoni et Material (11.SM) (Chou et al., 2020)(Chapter 11 Supplementary Kusunoki et al., 2019; Spinoni et al.,
al., 2019; Dunn et al., 2020). 2014a; Imbach et al., 2018) Material (11.SM) 2020; Coppola et al., 2021b)
(Chapter 11 Supplementary Material
(Chou et al., 2014a): RCM (Chou et al., 2014a): RCM (11.SM) .
simulations with Eta model driven simulations with Eta model driven
with 2 different GCMs. with 2 different GCMs. (Chou et al., 2014a): RCM simulations
with Eta model driven with 2 different
GCMs.
AGR Low confidence: Mixed Low confidence: Low confidence: Mixed signal in Medium confidence: Increase in High confidence: Increase in drought
ECOL signal. Mixed trends in Limited evidence. drought trends. Inconsistent drying drought based on total and surface severity with different metrics and
different subregions and in trend (but stronger tendency soil moisture (Xu et al., 2019a; high agreement between studies
different drought metrics, towards drying) based on total Cook et al., 2020)(Chapter 11 (Chapter 11 Supplementary Material
including soil moisture, PDSI- column soil moisture (Imbach et Supplementary Material (11.SM) (11.SM) (Cook et al., 2014b, 2020; Dai
PM and SPEI-PM (Greve et al., al., 2018; Xu et al., and on SPEI-PM (Naumann et al., et al., 2018; Lu et al., 2019; Vicente-
2014; Dai and Zhao, 2017; 2019a)(Chapter 11 2018; Xu et al., 2019a; Gu et al., Serrano et al., 2020a).
Spinoni et al., 2019; Padrón et Supplementary Material (11.SM) 2020).
al., 2020). and SPEI-PM (Naumann et al.,
2018; Gu et al., 2020).
HYDR Low confidence: Insufficient Low confidence: Low confidence: Limited Low confidence: Limited Medium confidence: Increase in
evidence (Dai and Zhao, 2017; Limited evidence. evidence. One study shows evidence. Inconsistent changes drought severity (Prudhomme et al.,
Gudmundsson et al., 2021). inconsistent changes (Touma et al., (Touma et al., 2015) or drying in 2014; Giuntoli et al., 2015; Touma et
Do Not Cite, Quote or Distribute 11-198 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
2015) part of region (Cook et al., 2020) al., 2015; Cook et al., 2020)
Caribbean MET Low confidence: Mixed Low confidence: Low confidence: Increase in Low confidence: Limited evidence Medium confidence: Increase in
(CAR) signal. Mixed trends between Limited evidence drought duration (Chou et al., and inconsistent changes. One drought duration (Chapter 11
subregions, but some evidence 2014a); inconsistent changes in study suggests increase in drought Supplementary Material (11.SM)
of increases in drought duration CDD (Chapter 11 Supplementary duration (Chou et al., 2014a), but (Nakaegawa et al., 2013; Chou et al.,
(Stephenson et al., 2014; Material (11.SM) CMIP6 projections show 2014a; Stennett-Brown et al., 2017;
McLean et al., 2015; Spinoni et inconsistent changes in CDD Coppola et al., 2021b)
al., 2019; Dunn et al., 2020). (Chapter 11 Supplementary
Material (11.SM)
AGR Low confidence: Mixed Low confidence: Low confidence: Inconsistent Medium confidence: Increase, but Medium confidence: Increase. Drying
ECOL signal. Mixed trends between Limited evidence trends in total column and surface including mixed signal in changes trend with surface soil moisture (Dai et
subregions with PDSI-PM and soil moisture (Chapter 11 of drought severity, with al., 2018; Lu et al., 2019), PDSI (Dai
SPEI-PM (Dai and Zhao, 2017; Supplementary Material (11.SM) , inconsistent trends in total soil et al., 2018) and SPEI-PM (Cook et al.,
Spinoni et al., 2019). and SPEI-PM (Naumann et al., moisture, (Chapter 11 2014b; Vicente-Serrano et al., 2020a).
2018; Gu et al., 2020). Supplementary Material (11.SM) , Total soil moisture shows weak
and drying trend based on SPEI-PM (Chapter 11 Supplementary Material
(Naumann et al., 2018; Gu et al., (11.SM) or no signal (Cook et al.,
2020). See also Chapter 12. 2020)
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited Low confidence: Mixed signal among
evidence. Limited evidence evidence. evidence. studies (Prudhomme et al., 2014;
Giuntoli et al., 2015; Touma et al.,
2015; Cook et al., 2020)
North- MET Low confidence: Mixed Low confidence: Low confidence: Inconsistent Low confidence: Mixed signal Medium confidence: Increase.
western signal. Mixed trends between Limited evidence trends (Chapter 11 Supplementary between different studies and Dominant signal is positive CDD trend
South subregions (Skansi et al., 2013; Material (11.SM) (Chou et al., models (Chou et al., 2014a; Touma (increasing dryness; Chapter 11
America Spinoni et al., 2019; 2014a; Touma et al., 2015; Xu et et al., 2015; Xu et al., 2019a; Supplementary Material (11.SM)); also
(NWS) Dereczynski et al., 2020; Dunn al., 2019a) Spinoni et al., 2020) (Chapter 11 some mixed signals between different
et al., 2020). Supplementary Material (11.SM) studies (Chou et al., 2014a; Duffy et
al., 2015; Touma et al., 2015; Spinoni
et al., 2020; Coppola et al., 2021b)
AGR Low confidence: Mixed trends Low confidence: Low confidence: Mixed trends Low confidence: Mixed signal in Low confidence: Mixed trend
ECOL between subregions and Limited evidence based on different metrics, changes in drought severity with between different drought metrics
drought metrics, including soil including decrease in total column drying in total column soil (Cook et al., 2014b, 2020; Dai et al.,
moisture, PDSI-PM and SPEI- soil moisture, (Chapter 11 moisture, (Chapter 11 2018; Lu et al., 2019; Vicente-Serrano
PM (Greve et al., 2014; Dai Supplementary Material (11.SM) , Supplementary Material (11.SM) , et al., 2020a) (Chapter 11
and Zhao, 2017; Spinoni et al., weak drying with surface soil lack of signal in the surface soil Supplementary Material (11.SM).
2019; Padrón et al., 2020) moisture (Xu et al., 2019a) and moisture (Xu et al., 2019a) and
wetting based on the SPEI-PM wetting trends with SPEI-PM
(Naumann et al., 2018; Gu et al., (Naumann et al., 2018; Gu et al.,
2020). 2020).
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited Low confidence: Lack of signal
evidence. Limited evidence. evidence. One study shows evidence. Inconsistent changes (Prudhomme et al., 2014; Giuntoli et
inconsistent changes (Touma et al., (Touma et al., 2015; Cook et al., al., 2015; Touma et al., 2015; Cook et
2015) 2020) al., 2020)
Do Not Cite, Quote or Distribute 11-199 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
Northern MET Low confidence: Mixed trends Low confidence: Medium confidence: Available Medium confidence: Increase in High confidence: Increase in drought
South between subregions, but some Limited evidence evidence suggests drying (Chapter drought severity (Chou et al., severity (Chou et al., 2014a; Duffy et
America evidence of increased drought 11 Supplementary Material (11.SM) 2014a; Touma et al., 2015; Xu et al., 2015; Touma et al., 2015; Marengo
(NSA) duration (Skansi et al., 2013; (Chou et al., 2014a; Touma et al., al., 2019a; Spinoni et al., 2020) and Espinoza, 2016; Spinoni et al.,
Marengo and Espinoza, 2016; 2015; Xu et al., 2019a). (Chapter 11 Supplementary 2020; Coppola et al., 2021b) (Chapter
Spinoni et al., 2019; Avila-Diaz Material (11.SM). 11 Supplementary Material (11.SM).
et al., 2020; Dereczynski et al.,
2020; Dunn et al., 2020)
AGR Low confidence: Mixed trends Low confidence: Medium confidence: Increase in Medium confidence: Increase. High confidence: Increase in drought
ECOL between subregions and Limited evidence drying. Tendency towards increase Tendency towards increase in severity with different metrics and
different drought metrics, in drought severity in total and drought severity in total soil high agreement between studies
including soil moisture, PDSI- surface soil moisture (Chapter 11 moisture (Chapter 11 (Chapter 11 Supplementary Material
PM and SPEI-PM, but some Supplementary Material (11.SM) Supplementary Material (11.SM) , (11.SM) (Cook et al., 2014b, 2020; Dai
evidence of decrease in drought (Xu et al., 2019a) inconsistent surface soil moisture (Xu et al., et al., 2018; Lu et al., 2019; Vicente-
severity (Greve et al., 2014; trends in studiesbased on the SPEI- 2019a) and SPEI-PM (Naumann et Serrano et al., 2020a).
Dai and Zhao, 2017; Spinoni et PM (Naumann et al., 2018; Gu et al., 2018; Gu et al., 2020).
al., 2019; Padrón et al., 2020) al., 2020).
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited High confidence: Increase in drought
evidence. Available evidence Limited evidence evidence. One study shows mixed evidence. Tendency to drying in severity (Prudhomme et al., 2014;
suggests lack of signal trends (Touma et al., 2015) two studies (Touma et al., 2015; Giuntoli et al., 2015; Touma et al.,
(Marengo and Espinoza, 2016; Cook et al., 2020) 2015; Cook et al., 2020)
Gudmundsson et al., 2021)
South MET Medium confidence: Increase Low confidence: Medium confidence: Increase Medium confidence: Increase in High confidence: Increase in
American in the frequency and severity of Limited evidence meteorological droughts (Chapter meteorological droughts (Chou et drought severity (Chou et al., 2014a;
Monsoon meteorological droughts based and recent droughts 11 Supplementary Material al., 2014a; Touma et al., 2015; Touma et al., 2015; Spinoni et al.,
(SAM) on SPI and CDD (Spinoni et as in 2010 were not (11.SM) (Chou et al., 2014a; Xu et al., 2019a) (Chapter 11 2020; Coppola et al., 2021b)
al., 2019; Avila-Diaz et al., attributed to Touma et al., 2015; Xu et al., Supplementary Material (11.SM). (Chapter 11 Supplementary Material
2020; Dereczynski et al., anthropogenic 2019a).. Drying trends in CDD in Drying trend in CDD in CMIP6 (11.SM).
2020). climate change CMIP6 and SPI in CMIP5 and SPI in CMIP5 (Touma et al.,
(Shiogama et al., (Touma et al., 2015; Xu et al., 2015; Xu et al., 2019a) but
2013). 2019a) but divergent trends in an divergent trends in an RCM
RCM driven by two GCMs (Chou driven by two GCMs (Chou et al.,
et al., 2014a) 2014a) and weak trends in
CMIP5-based SPI projections
(Spinoni et al., 2020).
AGR Low confidence: Mixed trends Low confidence: Medium confidence: Increase in High confidence: Increase in High confidence: Increase in
ECOL depending on subregions and Limited evidence agricultural and ecological drought severity with different drought severity with different
drought metrics, including soil droughts based on total column metrics (Naumann et al., 2018; metrics and high agreement between
moisture, PDSI-PM and SPEI- and surface soil moisture, Xu et al., 2019a; Gu et al., 2020) studies (Chapter 11 Supplementary
PM (Greve et al., 2014; Dai (Chapter 11 Supplementary (Chapter 11 Supplementary Material (11.SM) (Cook et al.,
and Zhao, 2017; Spinoni et al., Material (11.SM) (Xu et al., Material (11.SM). 2014b, 2020; Dai et al., 2018; Lu et
2019; Padrón et al., 2020) 2019a), and inconsistent signal al., 2019; Vicente-Serrano et al.,
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Final Government Distribution Chapter11 IPCC AR6 WGI
with SPEI-PM (Naumann et al., 2020a).
2018; Gu et al., 2020).
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited High confidence: Increase in
evidence. Available evidence Limited evidence evidence. One study shows evidence. Mixed signal (Touma drought severity (Prudhomme et al.,
suggests lack of signal mixed signal (Touma et al., et al., 2015) or tendency to drying 2014; Giuntoli et al., 2015; Touma et
(Gudmundsson et al., 2021) 2015) (Cook et al., 2020) al., 2015; Cook et al., 2020).
North- MET High confidence: Increase in Low confidence: Medium confidence: Increase of Medium confidence: Increase in Medium confidence: Increase in
eastern drought duration (Marengo et Low confidence in CDD (Chapter 11 Supplementary drought severity (Chou et al., drought severity (Chou et al., 2014a;
South al., 2017; Brito et al., 2018; human influence on Material (11.SM)(Chou et al., 2014a; Touma et al., 2015; Xu et Touma et al., 2015; Spinoni et al.,
America Spinoni et al., 2019; Avila-Diaz meteorological 2014a) and SPI (Xu et al. 2019, al., 2019a; Spinoni et al., 2020) 2020; Coppola et al., 2021b) (Chapter
(NES) et al., 2020; Dereczynski et al., drought in the Touma et al. 2015). Increase in (Chapter 11 Supplementary 11 Supplementary Material (11.SM).
2020; Dunn et al., 2020) region (Otto et al., CDD for change of +0.5°C in Material (11.SM).
2015b; Martins et global warming based on CMIP5
al., 2018). (Wartenburger et al., 2017)(SR15,
Ch3)
AGR Medium confidence: Increase Low confidence: Low confidence: Lack of signal Medium confidence: Increase. Medium confidence: Increase in
ECOL in drought severity based on Limited evidence based on different metrics, Dominant increase in drying with drought severity with different
different drought metrics, including total and surface some inconsistencies between metrics and high agreement between
including soil moisture, PDSI- column soil moisture, (Chapter 11 different drought metrics and different studies (Chapter 11
PM and SPEI-PM (Greve et al., Supplementary Material (11.SM) models (Naumann et al., 2018; Supplementary Material (11.SM)
2014; Dai and Zhao, 2017; (Xu et al., 2019a), and SPEI-PM Xu et al., 2019a; Gu et al., 2020) (Cook et al., 2014b, 2020; Dai et al.,
Spinoni et al., 2019; Padrón et (Naumann et al., 2018; Gu et al., (Chapter 11 Supplementary 2018; Lu et al., 2019; Vicente-
al., 2020) 2020). Material (11.SM) Serrano et al., 2020a).
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited Low confidence: Mixed signal
evidence. One study shows an Limited evidence evidence. One study shows a evidence. Weak drying (Touma among studies (Prudhomme et al.,
increase in drought severity weak drying (Touma et al., 2015) et al., 2015) or inconsistent trends 2014; Giuntoli et al., 2015; Touma et
(Gudmundsson et al., 2021) (Cook et al., 2020) al., 2015; Cook et al., 2020).
South- MET Medium confidence: Increase Medium confidence Low confidence: Inconsistent Low confidence: Mixed trends Medium confidence: Increase in
western in drought duration and that human-induced trends Increase in meteorological between studies and models. drought severity (Chou et al., 2014a;
South severity (Skansi et al., 2013; climate change has drought based on CDD in CMIP6 Increase in meteorological Touma et al., 2015; Spinoni et al.,
America Garreaud et al., 2017, 2020; contributed to long- GCMs (Chapter 11 drought based on CDD in CMIP6 2020).
(SWS) Saurral et al., 2017; Boisier et term trends and Supplementary Material (11.SM), GCMs (Chapter 11
al., 2018; Dereczynski et al., Central Chile but inconsistent trends in SPI in supplementary Material (11.SM) ,
2020; Dunn et al., 2020) drought between CMIP5 (Touma et al., 2015; Xu but inconsistent trends in SPI in
2010 and 2018 et al., 2019a) and substantial CMIP5 (Touma et al., 2015; Xu
(Boisier et al., 2016; model spread in Eta-RCM driven et al., 2019a) and substantial
Garreaud et al., with two GCMs (Chou et al., model spread in Eta-RCM driven
2020) 2014a). with two GCMs (Chou et al.,
2014a).
AGR Low confidence: Mixed trends Low confidence: Low confidence: Mixed trends Medium confidence: Increase in High confidence: Increase in
ECOL according to subregions and Limited evidence based on different metrics, drought severity based on total drought severity with different
different drought metrics, including decrease in total and surface soil moisture in metrics and high agreement between
including soil moisture, PDSI- column and surface soil moisture CMIP6 (Chapter 11 studies (Chapter 11 Supplementary
PM and SPEI-PM (Greve et al., in CMIP6 (Chapter 11 Supplementary Material (11.SM) Material (11.SM) (Cook et al.,
2014; Dai and Zhao, 2017; Supplementary Material (11.SM) and CMIP5 (Xu et al., 2019a), 2014b, 2020; Dai et al., 2018; Lu et
Spinoni et al., 2019; Padrón et , weak drying in total and surface and SPEI-PM (Naumann et al., al., 2019; Vicente-Serrano et al.,
al., 2020) soil moisture in CMIP5 (Xu et al., 2018; Gu et al., 2020). 2020a).
2019a), and weak signal based on
Do Not Cite, Quote or Distribute 11-201 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
the SPEI-PM (Naumann et al.,
2018; Gu et al., 2020).
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited High confidence: Increase in
evidence. General lack of Limited evidence evidence. One study shows evidence. Strong drying in (Cook drought severity (Prudhomme et al.,
signal in one study drying (Touma et al., 2015) et al., 2020); weak drying in 2014; Giuntoli et al., 2015; Touma et
(Gudmundsson et al., 2021) but (Touma et al., 2015) al., 2015; Cook et al., 2020).
streamflow decrease in
subregions in another study
(Boisier et al. (2018)
South- MET Low confidence: Mixed Low confidence: Low confidence: Inconsistent Low confidence: Mixed signals Low confidence: Mixed signals
eastern signals in observed trends Limited evidence. trends. Weak drying trend based between studies and models between studies and models (Chou et
South depending on subregion Wetting trend in on CDDCMIP6 (Chapter 11 (Chou et al., 2014a; Touma et al., al., 2014a; Touma et al., 2015;
America (Saurral et al., 2017; Knutson models and Supplementary Material (11.SM) 2015; Xu et al., 2019a; Spinoni et Spinoni et al., 2020; Coppola et al.,
(SES) and Zeng, 2018; Spinoni et al., observations in part , inconsistent trend between al., 2020) (Chapter 11 2021b) (Chapter 11 Supplementary
2019; Dereczynski et al., 2020; of region in one models based on SPI in CMIP5 Supplementary Material (11.SM) Material (11.SM).
Dunn et al., 2020) study (Knutson and (Touma et al., 2015; Xu et al., .
Zeng, 2018). 2019a) and lack of signal in study
with one RCM driven by two
GCMs (Chou et al., 2014a).
AGR Low confidence: Mixed trends Low confidence: Low confidence: Mixed trends Low confidence: Mixed signal in Low confidence: Mixed signals
ECOL according to subregions and Limited evidence based on different metrics, changes in drought severity with Inconsistent trends or lack of signal
different drought metrics, including lack of signal in total different metrics, (Chapter 11 in total and surface soil
including soil moisture, PDSI- column soil moisture, (Chapter 11 Supplementary Material (11.SM), moisture(Chapter 11 Supplementary
PM and SPEI-PM (Greve et al., Supplementary Material (11.SM) (Naumann et al., 2018; Xu et al., Material (11.SM) (Dai et al., 2018;
2014; Dai and Zhao, 2017; , weak drying with surface soil 2019a; Gu et al., 2020). Lu et al., 2019; Cook et al., 2020);
Spinoni et al., 2019; Padrón et moisture (Xu et al., 2019a) and decreasing drought severity in PDSI
al., 2020) wetting based on the SPEI-PM and SPEI-PM (Cook et al., 2014b;
(Naumann et al., 2018; Gu et al., Dai et al., 2018; Vicente-Serrano et
2020). al., 2020a).
HYDR Medium confidence: Low confidence: Low confidence: Limited Low confidence: Limited Low confidence: Mixed signal
Decrease. Reduction of Limited evidence evidence. One study shows evidence. Mixed signal (Touma among studies (Prudhomme et al.,
hydrological droughts (Dai and mixed signal (Touma et al., et al., 2015) or wetting (Cook et 2014; Giuntoli et al., 2015; Touma et
Zhao, 2017; Rivera and 2015) al., 2020) al., 2015; Cook et al., 2020).
Penalba, 2018)
Southern MET Medium confidence: Increase Low confidence: Low confidence: Lack of signal Medium confidence: Increase in Medium confidence: Increase in
South in the frequency of droughts Limited evidence (Chapter 11 Supplementary drought severity (Chou et al., drought severity (Chou et al., 2014a;
America (Skansi et al., 2013; Spinoni et Material (11.SM) (Chou et al., 2014a; Touma et al., 2015; Xu et Touma et al., 2015; Spinoni et al.,
(SSA) al., 2019; Dereczynski et al., 2014a). al., 2019a; Spinoni et al., 2020) 2020; Coppola et al., 2021b)
2020; Dunn et al., 2020). (Chapter 11 Supplementary (Chapter 11 Supplementary Material
Material (11.SM). (11.SM)
AGR Low confidence: Mixed trends Low confidence: Medium confidence: Increase in High confidence: Increase in High confidence: Increase in
ECOL depending on subregions and Limited evidence drought severity considering total drought severity (Naumann et al., drought severity with different
drought metrics, including soil column soil moisture, (Chapter 11 2018; Xu et al., 2019a; Gu et al., metrics and high agreement between
moisture, PDSI-PM and SPEI- Supplementary Material (11.SM) 2020) (Chapter 11 Supplementary studies (Chapter 11 Supplementary
PM (Greve et al., 2014; Dai , and surface soil moisture (Xu et Material (11.SM). Material (11.SM) (Cook et al.,
and Zhao, 2017; Spinoni et al., al., 2019a) and weak drying with 2014b, 2020; Dai et al., 2018; Lu et
2019; Padrón et al., 2020) the SPEI-PM (Naumann et al., al., 2019; Vicente-Serrano et al.,
2018; Gu et al., 2020). 2020a).
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited High confidence: Increase in
Do Not Cite, Quote or Distribute 11-202 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
evidence and lack of signal Limited evidence evidence. One study shows evidence. Drying (Touma et al., drought severity (Prudhomme et al.,
(Gudmundsson et al., 2021) drying (Touma et al., 2015) 2015; Cook et al., 2020) or 2014; Giuntoli et al., 2015; Touma et
inconsistent trend (Zhai et al., al., 2015; Cook et al., 2020)
2020b).
1
2 [END TABLE 11.15 HERE]
3
4
5 [START TABLE 11.16 HERE]
6
7 Table 11.16: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for temperature extremes in Europe,
8 subdivided by AR6 regions. See Sections 11.9.1 and 11.9.2 for details.
Detection and attribution; Projections
Region Observed trends
event attribution 1.5 °C 2 °C 4 °C
All Europe All subregions show a very Robust evidence of a human CMIP6 models project a CMIP6 models project a CMIP6 models project a
likely increase in the intensity contribution to the observed robust increase in the robust increase in the robust increase in the
and frequency of hot increase in the intensity and intensity and frequency of intensity and frequency of intensity and frequency of
extremes and decrease in the frequency of hot extremes TXx events and a robust TXx events and a robust TXx events and a robust
intensity and frequency of and decrease in the intensity decrease in the intensity and decrease in the intensity and decrease in the intensity and
cold extremes and frequency of cold frequency of TNn events (Li frequency of TNn events (Li frequency of TNn events (Li
extremes (Hu et al., 2020; et al., 2020). Median increase et al., 2020). Median increase et al., 2020). Median increase
Seong et al., 2020) of more than 0.5°C in the 50- of more than 1.5°C in the 50- of more than 5°C in the 50-
year TXx and TNn events year TXx and TNn events year TXx and TNn events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020) level (Li et al., 2020) level (Li et al., 2020)
Very likely increase in the Human influence very likely Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contributed to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Very likely (compared with Extremely likely (compared Virtually certain (compared
intensity and frequency of frequency of hot extremes the recent past (1995-2014)) with the recent past (1995- with the recent past (1995-
cold extremes and decrease in the intensity Extremely likely (compared 2014)) 2014))
and frequency of cold with pre-industrial) Virtually certain (compared Virtually certain (compared
with pre-industrial) with pre-industrial)
extremes
Decrease in the intensity and
frequency of cold extremes: Decrease in the intensity and Decrease in the intensity and
Very likely (compared with frequency of cold extremes: frequency of cold extremes:
the recent past (1995-2014)) Extremely likely (compared Virtually certain (compared
Extremely likely (compared with the recent past (1995- with the recent past (1995-
with pre-industrial) 2014)) 2014))
Virtually certain (compared Virtually certain (compared
with pre-industrial) with pre-industrial)
Greenland/Iceland (GIC) Significant increases in the Strong evidence of changes CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot from observations that are in increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant the direction of model frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and projected changes for the robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes future. The magnitude of and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
Do Not Cite, Quote or Distribute 11-203 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
(Peña-Angulo et al., 2020; projected changes increases et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Mernild et al., 2014; Sui et with global warming. increase of more than 0C in the Median increase of more than Median increase of more than
al., 2017; Dunn et al., 2020) 50-year TXx and TNn events 0.5°C in the 50-year TXx and 1°C in the 50-year TXx and
compared to the 1°C warming TNn events compared to the TNn events compared to the
level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 1°C in annual TXx and TNn 2020) and more than 1.5°C in 2020) and more than 2.5°C in
compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and CORDEX CMIP5 and CORDEX CMIP5 and CORDEX
simulations for an increase in the simulations for an increase in simulations for an increase in
intensity and frequency of hot the intensity and frequency of
the intensity and frequency of
extremes and decrease in the hot extremes and decrease in
intensity and frequency of cold the intensity and frequency of hot extremes and decrease in
extremes (Wehner et al., 2018; cold extremes (Wehner et al., the intensity and frequency of
Cardell et al., 2020). 2018; Cardell et al., 2020). cold extremes (Cardell et al.,
2020; Sillmann et al., 2013).
Very likely increase in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the observed increase in the Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of intensity and frequency of hot past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes extremes and decrease in the Very likely (compared with pre- Extremely likely (compared 2014))
intensity and frequency of industrial) with pre-industrial) Virtually certain (compared
cold extremes. with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial) with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
5 Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
Mediterranean (MED)
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Peña-Angulo et al., 2020; El and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Kenawy et al., 2013; for extremes (Seong et al., 2020); increase of more than 0.5°C in Median increase of more than Median increase of more than
Spain, Acero et al., 2014; (Wang et al., 2017c); (Sippel the 50-year TXx and TNn events 1°C in the 50-year TXx and 3.5°C in the 50-year TXx and
Fioravanti et al., 2016; Ruml and Otto, 2014); (Wilcox et compared to the 1°C warming TNn events compared to the TNn events compared to the
et al., 2017; Türkeş and Erlat, al., 2018) level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
2018; Donat et al., 2013, than 2°C in annual TXx and TNn 2020) and more than 2.5°C in 2020) and more than 5°C in
5
This region includes both northern Africa and southern Europe
Do Not Cite, Quote or Distribute 11-204 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
2014, 2016; Filahi et al., compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
2016; Driouech et al., 2020; (Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Dunn et al.2020)
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5 and RCM simulations
an increase in the intensity and for an increase in the intensity for an increase in the intensity
and frequency of hot extremes
frequency of hot extremes and and frequency of hot extremes
and decrease in the intensity
decrease in the intensity and and decrease in the intensity and frequency of cold extremes
frequency of cold extremes and frequency of cold extremes (Cardoso et al., 2019; Nastos
(Cardoso et al., 2019; Zollo et (Cardoso et al., 2019; Tomozeiu and Kapsomenakis, 2015;
al., 2016); Weber et al., 2018) et al., 2014; Tomozeiu et al., 2014; Cardell
Abaurrea et al., 2018; Nastos et al., 2020; Zollo et al., 2016;
and Kapsomenakis, 2015; Giorgi et al., 2014; Driouech et
Cardell et al., 2020; Zollo et al., al., 2020; Coppola et al., 2021a;
2016; Weber et al., 2018; Engelbrecht et al., 2015)
Coppola et al., 2021a)
Very likely increase in the Human influence likely Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contributed to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
and frequency of cold industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
extremes
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial) with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Western and Central Europe Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
(WCE) intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Christidis et al., 2015; and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Scherrer et al., 2016; extremes (Seong et al., 2020; increase of more than 0.5°C in Median increase of more than Median increase of more than
Shevchenko et al., 2014; Wang et al., 2017; Sippel et the 50-year TXx and TNn events 1.5°C in the 50-year TXx and 5.5°C in the 50-year TXx and
Twardosz and Kossowska- al., 2017, 2018; Dong et al., compared to the 1°C warming TNn events compared to the TNn events compared to the
Cezak, 2013; Dunn et al., 2014, 2016; Sippel et al., level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
2020) 2016; Christidis et al., 2015; than 2°C in annual TXx and TNn 2020) and more than 3°C in 2020) and more than 6°C in
Cattiaux and Ribes, 2018; compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
Leach et al., 2020) (Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
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Final Government Distribution Chapter11 IPCC AR6 WGI
CMIP5 and RCM simulations for CMIP5 and RCM simulations CMIP5 and RCM simulations
an increase in the intensity and for an increase in the intensity for an increase in the intensity
frequency of hot extremes (Lau and frequency of hot extremes and frequency of hot extremes
and Nath, 2014; Lhotka et al., (Russo et al., 2015; Lau and (Lau and Nath, 2014; Lhotka et
2018) Nath, 2014; Lhotka et al., 2018) al., 2018)
Very likely increase in the Human influence likely Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contributed to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
and frequency of cold industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
extremes
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial) with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Eastern Europe (EEU) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Peña-Angulo et al., 2020; and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Zhang et al., 2019b; Donat et extremes (Seong et al., 2020; increase of more than 0.5°C in Median increase of more than Median increase of more than
al., 2016; Dunn et al., 2020) Wang et al., 2017; Sippel and the 50-year TXx and TNn events 1.5°C in the 50-year TXx and 4.5°C in the 50-year TXx and
Otto, 2014; Leach et al., compared to the 1°C warming TNn events compared to the TNn events compared to the
2020; Hauser et al., 2016) level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
than 2°C in annual TXx and TNn 2020) and more than 2.5°C in 2020) and more than 5.5°C in
compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and CORDEX CMIP5 and CORDEX CMIP5 and CORDEX
simulations for an increase in the simulations for an increase in simulations for an increase in
intensity and frequency of hot the intensity and frequency of the intensity and frequency of
extremes and decrease in the hot extremes and decrease in hot extremes and decrease in
intensity and frequency of cold the intensity and frequency of the intensity and frequency of
extremes (Wehner et al., 2018; cold extremes (Wehner et al., cold extremes (Cardell et al.,
Cardell et al., 2020). 2018; Cardell et al., 2020; 2020; Khlebnikova et al., 2019;
Khlebnikova et al., 2019). Sillmann et al., 2013)
Very likely increase in the Human influence likely Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contributed to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
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intensity and frequency of and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
cold extremes and frequency of cold industrial) with pre-industrial) Virtually certain (compared
extremes with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial) with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
Northern Europe (NEU) Significant increases in the Robust evidence of a human CMIP6 models project a robust CMIP6 models project a robust CMIP6 models project a robust
intensity and frequency of hot contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
extremes and significant increase in the intensity and frequency of TXx events and a frequency of TXx events and a frequency of TXx events and a
decreases in the intensity and frequency of hot extremes robust decrease in the intensity robust decrease in the intensity robust decrease in the intensity
frequency of cold extremes and decrease in the intensity and frequency of TNn events (Li and frequency of TNn events and frequency of TNn events
(Matthes et al., 2015; and frequency of cold et al., 2020; Annex). Median (Li et al., 2020; Annex). (Li et al., 2020; Annex).
Vikhamar-Schuler et al., extremes (Seong et al., 2020; increase of more than 0.5°C in Median increase of more than Median increase of more than
2016; Dunn et al., 2020) Wang et al., 2017; Otto et al., the 50-year TXx and TNn events 1.5°C in the 50-year TXx and 4.5°C in the 50-year TXx and
2012; Massey et al., 2012; compared to the 1°C warming TNn events compared to the TNn events compared to the
Christiansen et al., 2018; level (Li et al., 2020) and more 1°C warming level (Li et al., 1°C warming level (Li et al.,
King et al., 2015; Roth et al., than 1.5°C in annual TXx and 2020) and more than 2.5°C in 2020) and more than 4.5°C in
2018) TNn compared to pre-industrial annual TXx and TNn compared annual TXx and TNn compared
(Annex). to pre-industrial (Annex). to pre-industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 simulations for an CMIP5 simulations for an CMIP5 simulations for an
increase in the intensity and increase in the intensity and increase in the intensity and
frequency of hot extremes and frequency of hot extremes and frequency of hot extremes and
decrease in the intensity and decrease in the intensity and decrease in the intensity and
frequency of cold extremes frequency of cold extremes frequency of cold extremes
(Jacob et al., 2018; Laliberté et (Jacob et al., 2018; Laliberté et (Jacob et al., 2018; Laliberté et
al., 2015; Sigmond et al., 2018; al., 2015; Sigmond et al., 2018; al., 2015; Sigmond et al., 2018;
Dosio and Fischer, 2018; Dosio and Fischer, 2018; Dosio and Fischer, 2018;
Forzieri et al., 2016) Forzieri et al., 2016) Forzieri et al., 2016)
Very likely increase in the Human influence likely Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contributed to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the recent Very likely (compared with the Virtually certain (compared
intensity and frequency of frequency of hot extremes past (1995-2014)) recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with pre- Extremely likely (compared 2014))
and frequency of cold industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
extremes
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the recent Very likely (compared with the frequency of cold extremes:
past (1995-2014)) recent past (1995-2014)) Virtually certain (compared
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Final Government Distribution Chapter11 IPCC AR6 WGI
Very likely (compared with pre- Extremely likely (compared with the recent past (1995-
industrial) with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
1
2 [END TABLE 11.16 HERE]
3
4
5 [START TABLE 11.17 HERE]
6
7 Table 11.17: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for heavy precipitation in Europe, subdivided
8 by AR6 regions. See Sections 11.9.1 and 11.9.3 for details.
Detection and attribution; Projections
Region Observed trends
event attribution 1.5 °C 2 °C 4 °C
All Europe Significant intensification of Robust evidence of a human CMIP6 models project an CMIP6 models project a CMIP6 models project a
heavy precipitation (Sun et contribution to the observed increase in the intensity and robust increase in the robust increase in the
al., 2020) intensification of heavy frequency of heavy intensity and frequency of intensity and frequency of
precipitation (Paik et al., precipitation (Li et al., heavy precipitation (Li et al., heavy precipitation (Li et al.,
2020) 2020a). Median increase of 2020a). Median increase of 2020a). Median increase of
more than 0% in the 50-year more than 2% in the 50-year more than 8% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) level (Li et al., 2020a) level (Li et al., 2020a)
Likely intensification of heavy Human influence likely Intensification of heavy Intensification of heavy Intensification of heavy
precipitation contributed to the observed precipitation: precipitation: precipitation:
intensification of heavy High confidence (compared Likely (compared with the Very likely (compared with
precipitation with the recent past (1995- recent past (1995-2014)) the recent past (1995-2014))
2014)) Very likely (compared with Extremely likely (compared
Likely (compared with pre- pre-industrial) with pre-industrial)
industrial)
Greenland/Iceland (GIC) Intensification of heavy Limited evidence CMIP6 models project an CMIP6 models project a robust CMIP6 models project a robust
precipitation (Peña-Angulo et increase in the intensity and increase in the intensity and increase in the intensity and
al., 2020) frequency of heavy frequency of heavy frequency of heavy
precipitation (Li et al., 2020; precipitation (Li et al., 2020; precipitation (Li et al., 2020;
Annex). Median increase of Annex). Median increase of Annex). Median increase of
more than 2% in the 50-year more than 8% in the 50-year more than 30% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and more level (Li et al., 2020a) and more level (Li et al., 2020a) and more
than 10% in annual Rx1day, than 15% in annual Rx1day, than 30% in annual Rx1day and
Rx5day, and Rx30day Rx5day, and Rx30day Rx5day and 35% in annual
compared to pre-industrial compared to pre-industrial Rx30day compared to pre-
(Annex). (Annex). industrial (Annex).
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Additional evidence from Additional evidence from Additional evidence from
CMIP5 and CORDEX CMIP5 and CORDEX CMIP5 and CORDEX
simulations for an increase in simulations for an increase in simulations for an increase in
the intensity of heavy the intensity of heavy the intensity of heavy
precipitation (Cardell et al., precipitation (Cardell et al., precipitation (Cardell et al.,
2020) 2020) 2020)
Medium confidence in the Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
intensitification of heavy precipitation: precipitation: precipitation:
precipitation High confidence (compared Likely (compared with the Extremely likely (compared
with the recent past (1995- recent past (1995-2014)) with the recent past (1995-
2014)) Very likely (compared with pre- 2014))
Likely (compared with pre- industrial) Virtually certain (compared
industrial) with pre-industrial)
6 Lack of agreement on the Limited evidence (Añel et al., CMIP6 models, CMIP5 CMIP6 models project a CMIP6 models project a
Mediterranean (MED)
evidence of trends (Sun et al., 2014; U.S. Department of models, and RCMs project robust increase in the robust increase in the
2020; Dunn et al., 2020; Agriculture Economic inconsistent changes in the intensity and frequency of intensity and frequency of
Casanueva et al., 2014; de Research Service, 2016) region (Li et al., 2020; heavy precipitation (Li et al., heavy precipitation (Li et al.,
Lima et al., 2015; Gajić- Cardell et al., 2020; Zollo et 2020; Annex). Median 2020; Annex). Median
Čapka et al., 2015; Ribes et al., 2016; Samuels et al., increase of more than 2% in increase of more than 8% in
al., 2019; Peña-Angulo et al., 2018) the 50-year Rx1day and the 50-year Rx1day and
2020; Jacob et al., 2018; Rx5day events compared to Rx5day events compared to
Rajczak and Schär, 2017; the 1°C warming level (Li et the 1°C warming level (Li et
Coppola et al., 2021a; Donat al., 2020a) and more than 0% al., 2020a) and more than 2%
et al., 2014; Mathbout et al., in annual Rx1day and Rx5day in annual Rx1day and Rx5day
2018) and less than -2% in annual and less than -2% in annual
Rx30day compared to pre- Rx30day compared to pre-
industrial (Annex). industrial (Annex).
Additional evidence from Additional evidence from
CMIP5 and RCM simulations CMIP5 and RCM simulations
for an increase in the intensity for an increase in the intensity
of heavy precipitation of heavy precipitation
(Cardell et al., 2020; Zollo et (Cardell et al., 2020;
al., 2016; Samuels et al., Tramblay and Somot, 2018;
2018) Zollo et al., 2016; Samuels et
al., 2018; Monjo et al., 2016;
Rajczak et al., 2013; Coppola
et al., 2021b; Driouech et al.,
2020)
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Low confidence (compared Medium confidence High confidence (compared
with the recent past (1995- (compared with the recent with the recent past (1995-
2014)) past (1995-2014)) 2014))
6
This region includes both northern Africa and southern Europe
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Final Government Distribution Chapter11 IPCC AR6 WGI
Medium confidence High confidence (compared High confidence (compared
(compared with pre- with pre-industrial) with pre-industrial)
industrial)
Western and Central Europe Intensification of heavy Disagreement among studies CMIP6 models project an CMIP6 models project an CMIP6 models project a robust
(WCE) precipitation (Sun et al., (Wilcox et al., 2018; Philip et increase in the intensity and increase in the intensity and increase in the intensity and
2020; Casanueva et al., 2014; al., 2018; Schaller et al., frequency of heavy frequency of heavy frequency of heavy
Croitoru et al., 2013; Fischer 2014, Vautard et al., 2015) precipitation (Li et al., 2020; precipitation (Li et al., 2020; precipitation (Li et al., 2020;
et al., 2015; Roth et al., 2014; Annex). Median increase of Annex). Median increase of Annex). Median increase of
Willems, 2013). more than 0% in the 50-year more than 2% in the 50-year more than 10% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and more level (Li et al., 2020a) and more level (Li et al., 2020a) and more
than 6% in annual Rx1day and than 8% in annual Rx1day and than 15% in annual Rx1day and
Rx5day and 4% in annual Rx5day and 6% in annual Rx5day and 10% in annual
Rx30day compared to pre- Rx30day compared to pre- Rx30day compared to pre-
industrial (Annex). industrial (Annex). industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations CMIP5 and RCM simulations CMIP5 and RCM simulations
for an increase in the intensity for an increase in the intensity for an increase in the intensity
of heavy precipitation (Rajczak of heavy precipitation (Rajczak of heavy precipitation (Rajczak
and Schär, 2017; Donnelly et and Schär, 2017; Donnelly et and Schär, 2017; Madsen et al.,
al., 2017) al., 2017) 2014)
Medium confidence in the Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
intensification of havy precipitation: precipitation: precipitation:
precipitation Medium confidence (compared High confidence (compared Very likely (compared with the
with the recent past (1995- with the recent past (1995- recent past (1995-2014))
2014)) 2014)) Extremely likely (compared
High confidence (compared Likely (compared with pre- with pre-industrial)
with pre-industrial) industrial)
Eastern Europe (EEU) Significant intensification of Limited evidence CMIP6 models project an CMIP6 models project an CMIP6 models project a robust
heavy precipitation (Sun et increase in the intensity and increase in the intensity and increase in the intensity and
al., 2020; Dunn et al., 2020; frequency of heavy frequency of heavy frequency of heavy
Ashabokov et al., 2017) precipitation (Li et al., 2020; precipitation (Li et al., 2020; precipitation (Li et al., 2020;
Annex). Median increase of Annex). Median increase of Annex). Median increase of
more than 2% in the 50-year more than 4% in the 50-year more than 15% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) and more level (Li et al., 2020a) and more level (Li et al., 2020a) and more
than 6% in annual Rx1day and than 8% in annual Rx1day and than 15% in annual Rx1day and
Rx5day and 4% in annual Rx5day and 6% in annual Rx5day and 10% in annual
Rx30day compared to pre- Rx30day compared to pre- Rx30day compared to pre-
industrial (Annex). industrial (Annex). industrial (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and CORDEX CMIP5 and CORDEX CMIP5/CMIP3 and CORDEX
simulations for an increase in simulations for an increase in simulations for an increase in
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Final Government Distribution Chapter11 IPCC AR6 WGI
the intensity of heavy the intensity of heavy the intensity of heavy
precipitation (Cardell et al., precipitation (Cardell et al., precipitation (Cardell et al.,
2020) 2020) 2020; Rajczak et al., 2013)
High confidence in the Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
intensification of havy precipitation: precipitation: precipitation:
precipitation Medium confidence (compared High confidence (compared Very likely (compared with the
with the recent past (1995- with the recent past (1995- recent past (1995-2014))
2014)) 2014)) Extremely likely (compared
High confidence (compared Likely (compared with pre- with pre-industrial)
with pre-industrial) industrial)
Northern Europe (NEU) Significant intensification of Robust evidence of a human CMIP6 models project an CMIP6 models project an CMIP6 models project a robust
heavy precipitation (Sun et contribution to the observed increase in the intensity and increase in the intensity and increase in the intensity and
al., 2020; Dunn et al., 2020) intensification of heavy frequency of heavy frequency of heavy frequency of heavy
precipitation in winter precipitation (Li et al., 2020; precipitation (Li et al., 2020; precipitation (Li et al., 2020;
(Schaller et al., 2016; Vautard Annex). Median increase of Annex). Median increase of Annex). Median increase of
et al., 2016; Otto et al., more than 0% in the 50-year more than 4% in the 50-year more than 15% in the 50-year
2018b), but not in summer Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
(Schaller et al., 2014; Otto et compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
al., 2015c; Wilcox et al., level (Li et al., 2020a) and more level (Li et al., 2020a) and more level (Li et al., 2020a) and more
2018) than 6% in annual Rx1day, than 8% in annual Rx1day and than 15% in annual Rx1day,
Rx5day, and Rx30day Rx5day and 6% in annual Rx5day, and Rx30day
compared to pre-industrial Rx30day compared to pre- compared to pre-industrial
(Annex). industrial (Annex). (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations CMIP5 and RCM simulations CMIP5 and RCM simulations
for an increase in the intensity for an increase in the intensity for an increase in the intensity
of heavy precipitation of heavy precipitation of heavy precipitation (Madsen
(Donnelly et al., 2017) (Donnelly et al., 2017; Ramos et al., 2014; Ramos et al., 2016;
et al., 2016; Romero and Romero and Emanuel, 2017;
Emanuel, 2017) Donnelly et al., 2017)
High confidence in the High confidence in a human Intensification of heavy Intensification of heavy Intensification of heavy
intensification of heavy contribution to the observed precipitation: precipitation: precipitation:
precipitation intensification of heavy Medium confidence (compared High confidence (compared Very likely (compared with the
precipitation in winter. with the recent past (1995- with the recent past (1995- recent past (1995-2014))
High confidence in the 2014)) 2014)) Extremely likely (compared
changes in flood seasonality High confidence (compared Likely (compared with pre- with pre-industrial)
with pre-industrial) industrial)
High confidence in the
increase in extreme snow-
melt events
1
2 [END TABLE 11.17 HERE]
3
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1
2 [START TABLE 11.18 HERE]
3
4 Table 11.18: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for meteorological droughts (MET),
5 agricultural and ecological droughts (AGR/ECOL), and hydrological droughts (HYDR) in Europe, subdivided by AR6 regions. See Sections 11.9.1 and 11.9.4 for
6 details.
Detection and Projections
Region and drought
Observed trends attribution; event
types +1.5 °C +2 °C +4 °C
attribution
Greenland/ MET Low confidence: Limited Low confidence: Low confidence: Limited evidence Low confidence: Limited evidence given Low confidence: Limited evidence given
Iceland evidence, given limited number of Limited evidence given limited number of studies limited number of studies (Walsh et al., limited number of studies (Walsh et al., 2020) ;
(GIC) studies and limited data (Walsh et because of lack of (Walsh et al., 2020); tendency to 2020); tendency to decrease in tendency to decrease in meteorological drought
al., 2020; Dunn et al., 2020) studies decrease in meteorological drought meteorological drought based on CDD based on CDD (Chapter 11 Supplementary
based on CDD (Chapter 11 (Chapter 11 Supplementary Material Material (11.SM)) and SPI (Touma et al., 2015);
Supplementary Material (11.SM)) and (11.SM)) and SPI (Touma et al., 2015) ; also also consistent with mixed index combining SPI
SPI (Touma et al., 2015) consistent with mixed index combining SPI and SPEI in Iceland (Spinoni et al., 2018b)
and SPEI in Iceland (Spinoni et al., 2018b)
Based on (Spinoni et al., 2018b) in Iceland [11
Based on (Spinoni et al., 2018b) in Iceland EUROCORDEX RCPs 4.5 AND 8.5] Based on
[11 EUROCORDEX RCPs 4.5 AND 8.5] the Standardized Precipitation Index: Decrease
Based on the Standardized Precipitation of drought frequency.
Index: Decrease of drought frequency.
AGR Low confidence: Limited Low confidence: Low confidence: Limited evidence Low confidence: Limited evidence because Low confidence: Limited evidence because of
ECOL evidence, given limited number of Limited evidence because of lack of studies (Walsh et of lack of studies (Walsh et al., 2020) and lack of studies (Walsh et al., 2020) and
studies and limited data (Walsh et because of lack of al., 2020) and inconsistent changes inconsistent changes in soil moisture in inconsistent changes in soil moisture in CMIP6
al., 2020).
studies in soil moisture in CMIP6 (Chapter CMIP6 (Chapter 11 Supplementary Material (Chapter 11 Supplementary Material (11.SM))
11 Supplementary Material (11.SM))
(11.SM))
HYDR Low confidence: Limited evidence Low confidence: Low confidence: Limited evidence Low confidence: Limited evidence because Low confidence: Limited evidence because of
given limited number of studies Limited evidence because of lack of studies of lack of studies lack of studies
and limited data (Walsh et al., because of lack of
2020) studies
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Final Government Distribution Chapter11 IPCC AR6 WGI
MET
Mediter- Low confidence: Mixed signals. Low confidence: Mixed Medium confidence: Increase. With Medium confidence: Increase. With medium High confidence: Increase. With high confidence
ranean Observed land precipitation trends signals. There are mixed medium confidence both CMIP5 and confidence both CMIP5 and CMIP6 show a both CMIP5 and CMIP6 (and EURO-CORDEX)
7 show pronounced variability within signals within the region
(MED) CMIP6 show a decline in winter and decline in winter and summer total show a decline in winter and summer total
the region, with magnitude and sign and low confidence in
of trend in the past centrury human influence on summer total precipitation and precipitation and increase in number of CDD precipitation and increase in number of CDD.
depending on time period (Donat et meteorological drought increase in number of CDD (percentage precipitation change per degree of Drought intensity and frequency increase with
al., 2014a; Stagge et al., 2017; over MED (Kelley et al., (percentage precipitation change per local warming is with high confidence larger in high confidence, particularly in the southern
Zittis, 2017; Mathbout et al., 2015; Gudmundsson and degree of local warming is with high JJA than DJF) (Interactive Atlas, Cardell et al., Mediterranean (Samuels et al., 2018; Cardell et al.,
2018a). There is low confidence in Seneviratne, 2016; confidence larger in JJA than DJF) 2020; Li et al., 2020)(Chapter 11 2020; Cook et al., 2020; Li et al., 2020a; Spinoni
an increase of drought frequency Knutson and Zeng, 2018; (Interactive Atlas, Cardell et al., 2020; Supplementary Material (11.SM)). Also weak et al., 2020; Coppola et al., 2021a)( Chapter 11
and severity based on SPI (Spinoni Wilcox et al., 2018)
Li et al., 2020)(Chapter 11 increase in meteorological drought based on Supplementary Material (11.SM); Interactive
et al., 2015; Gudmundsson and
Seneviratne, 2016; Peña-Angulo et Supplementary Material (11.SM)). SPI (Touma et al., 2015; Xu et al., 2019a). Atlas) (Driouech et al., 2020)
al., 2020; Vicente‐Serrano et al., Also weak increase in meteorological
2021; MedECC, 2020; Driouech et drought based on SPI (Touma et al.,
al., 2021) 2015; Xu et al., 2019a).
AGR
ECOL Medium confidence: Increase. Medium confidence: of Medium confidence: Drought High confidence: Drought increase for pre- Very likely: Drought increase for pre-industrial
attribution of increasing increase for pre-industrial and recent industrial and recent past baselines. and recent past baselines.
Increases in probability and trend in ecological and
past baselines.
intensity of agricultural and agricultural drought,
ecological droughts based on soil based on soil moisture
moisture and water-balance deficits, and water-balance Recent past baseline: Recent past baseline: Recent past baseline:
but weakers signals in some studies metrics (Mariotti et al.,
(Greve et al., 2014; Hanel et al., 2015; García-Herrera et Decreasing soil water availability Decreasing soil water availability during Based on projections at +3°C: Large decreasing
2018; García-Herrera et al., 2019; al., 2019; Marvel et al., during drought events compared to drought events compared to 1971-2000, even soil water availability during drought events
Moravec et al., 2019; Padrón et al., 2019; Padrón et al., 1971-2000, even when accounting for when accounting for adaptation to mean compared to 1971-2000, even when accounting for
2020; Markonis et al., 2021). Also 2020)
adaptation to mean conditions conditions; about twice larger signal compared adaptation to mean conditions; more than three
increases based on analyses using
the Standardized Precipitation García-Herrera et al. (Samaniego et al., 2018). to response at +1.5°C (Samaniego et al., 2018). times larger signal compared to response at
Evapotranspiration Index (SPEI) (2019): Attribution of +1.5°C (Samaniego et al., 2018).
and the Palmer Drought Severity the 2016/2017 drought in Increasing drought duration and Increasing drought duration and frequency
Index (PDSI). Increase of drought southwestern Europe to frequency compared to 1971-2000 compared to 1971-2000, with about twice
severity in South Europe (Stagge et climate change based on (Xu et al., 2019a) larger signal compared to response at +1.5°C Based on projections at +3°C:About five-fold
al., 2017; Spinoni et al., 2019; Dai NCEP trends in soil (Xu et al., 2019a) increase in drought magnitude based on SPEI-PM
and Zhao, 2017), the Iberian moisture for weather
Increasing drought magnitude based compared to +0.6°C baseline, using simulations
Peninsula (Vicente-Serrano et al., anologues to 2016/2017
2014; González-Hidalgo et al., event. on SPEI-PM compared to +0.6°C Increasing drought magnitude based on SPEI- within single ESM driven with sea surface
2018). baseline, using simulations within PM compared to +0.6°C baseline, using temperature and sea ice conditions of 7 ESMs
Mariotti et al. (2015): single ESM driven with sea surface simulations within single ESM driven with sea (Naumann et al., 2018)
(Markonis et al., 2021): Increase in Attributable trend to CC: temperature and sea ice conditions of surface temperature and sea ice conditions of 7
duration of agricultural droughts Decrease in soil moisture 7 ESMs (Naumann et al., 2018) ESMs (Naumann et al., 2018)
based on soil moisture déficits from in summer that agrees
Pre-industrial baseline:
1901-2015. with CMIP5 models.
7
This region includes both northern Africa and southern Europe
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Final Government Distribution Chapter11 IPCC AR6 WGI
However: no emergence Pre-industrial baseline:
(García-Herrera et al., 2019): yet in soil moisture or P- Pre-industrial baseline: Strong decreases of surface and total soil moisture,
Increase in soil moisture anomalies E at grid cell scale (see Decrease in soil moisture during in both spring-summer (AMJJAS) and fall-winter
for weather analogues to 2016/2017 CC-Box A.1. on
drought events in CMIP6 models at Decreases of surface and total soil moisture, in (ONDJFM) half years, with about twice larger
drought events in 1985-2018 vs Uncertainty).
1948-1984. +1.5°C vs pre-industrial baseline both AMJJAS and ONDJFM half years (Cook response compared to +2°C (Cook et al., 2020)
Padrón et al. (2020): (Chapter 11 Supplementary Material et al., 2020)
(Padrón et al., 2020): Weak signals Increasing drying trend (11.SM)) Very large decrease in soil moisture during
in water-balance (precipitation- in P-E during dry season Decrease in soil moisture during drought drought events in CMIP6 models at +4°C vs pre-
evapotranspiration) deficits in the over land areas, events in CMIP6 models at +2°C vs pre- industrial baseline (Chapter 11 Supplementary
dry season (1985-2014)-(1902- including in industrial baseline (Chapter 11 Supplementary Material (11.SM))
1950) Mediterranean region
Material (11.SM))
(but attribution done at
(Greve et al., 2014): Increase in global scale, not regional
water-balance (precipitation- scale)
evapotranspiration) deficits on
annual scale, (1985-2005) - (1948- Marvel et al. (2019):
1968) Attributable drying trend
in larger continental
(Hanel et al., 2018): Significant region with tree-ring
decrease in soil moisture in data including strong
Southern Europe from 1766-2015 signal in Mediterranean
from hydrological model driven from 1900-1950 and
with reconstructed meteorological currently increasing
data. again after masking from
aerosols.
HYDR
High confidence: Increase in Medium confidence: Medium confidence: Increase in High confidence: Increase. Very likely: Increase
frequency and severity of Increase. Model-based hydrological drought for both pre-
hydrological droughts, particularly assessment shows with industrial and recent past baseline Recent past baseline:
Recent past baseline:
in northern part of the domain medium confidence a
(Lorenzo-Lacruz et al., 2013; Dai human fingerprint on Recent past baseline: Forzieri et al. (2014) [LISFLOOD
and Zhao, 2017; Gudmundsson et increased hydrological simulations driven by 12 RCM-GCM pairs Forzieri et al. (2014) [LISFLOOD simulations
al., 2017, 2019, 2021) (Section drought, related to rising Forzieri et al. (2014): 20 yr deficit using CMIP3 GCMs]: Strong increase in the driven by 12 RCM-GCM pairs using CMIP5
8.3.1.6). temperature and volumes are projected to increase 20-yr return level minimum flow and deficit GCMs]: Strong increase in the 20-yr return level
atmospheric demand by 50% by the 2020s compared to volumes in 2050 in A1B scenario compared minimum flow and deficit volumes in 2080 in
(Gudmundsson et al., 1961-1990 (based on simulations to 1961-1990. A1B scenario compared to 1961-1990
2017, 2021) and recent with LISFLOOD model driven by
events. There is medium 12 RCM simulations with different Roudier et al. (2016) [11 RCMs]: Increase in
confidence that change GCM-RCM pairs; CMIP3 GCMs, the severity of the low flows at +2°C Prudhomme et al. (2014) [5 CMIP5 models
in land use and terrestrial A1B scenario). Frequency of compared to 1971-2000 conditions in the driving 7 global impact models. RCP8.5, 2070-
water management hydrological droughts is projected Iberian Peninsula, Southern France and 2099] Strong increase (40-60%) of dry days
contribute to trends in to increase (Touma et al., 2015). Greece. compared to 1976-2005
hydrological drought
(Teuling et al., 2019; Schewe et al. (2014). Decrease between 30- Giuntoli et al. (2015) (5 CMIP5 models driving
Vicente-Serrano et al., 50% of the annual runoff compared to 1980- 6 global hydrology models): 50-60% increase in
2019) 2010. frequency of days under low flow in 2066-2099
compared to 1972-2005. Strong signal to noise
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Final Government Distribution Chapter11 IPCC AR6 WGI
Touma et al. (2015): Increase in the ratio in terms of model agreement, strongest hot
frequency of hydrological droughts relative spot globally.
to 1961-2005.
Pre-industrial baseline
Pre-industrial baseline Cook et al. (2020) [13 CMIP6 models and
SSP3-7.0. Very strong decrease (40-60%) of
Cook et al. (2020) [13 CMIP6 models and
SSP1-2.6] Decrease in surface and total total runoff in spring-summer half-year in
runoff, in both spring-summer (AMJJAS) southern Europe. Also strong decreases (>20%)
and fall-winter (ONDJFM) half years, with for total runoff in fall-winter half-year, and for
strongest decreases for total runoff in spring- surface runoff in both half years (AMJJAS,
summer half -year- ONDJFM).
Western MET Low confidence: Limited evidence Low confidence: No Low confidence: Inconsistent signal Low confidence: Inconsistent signal, but with Medium confidence: Increase based on CDD
and Central in change in severity. Small and signal or varying signal in CDD in CMIP6 (Chapter 11 weak tendency to drying in CDD in CMIP6 (Chapter 11 Supplementary Material (11.SM)).
Europe non-significant changes and some depending on considered Supplementary Material (11.SM)) and (Chapter 11 Supplementary Material (11.SM)) Also partial drying based on CMIP5 SPI, but
(WCE) dependency on season and location. index (Gudmundsson
in SPI in CMIP5 (Orlowsky and and SPI in CMIP5 (Orlowsky and strong geographical gradients and trends in part
Small and non significant changes and Seneviratne, 2016;
in the frequency of dry spells Hauser et al., 2017) Seneviratne, 2013; Touma et al., Seneviratne, 2013; Touma et al., 2015; Xu et not signficant (Orlowsky and Seneviratne, 2013;
(Zolina et al., 2013), CDD (Dunn et 2015; Xu et al., 2019a). al., 2019a) Touma et al., 2015; Vicente-Serrano et al., 2020a).
al., 2020), and in drought severity Summer decrease in wet day projected in
(SPI) (Orlowsky and Seneviratne, Switzerland (Fischer et al., 2015).
2013; Stagge et al., 2017; Caloiero
et al., 2018; Spinoni et al., 2019);
but wet days decrease in summer
(Gobiet et al., 2014).
AGR Medium confidence: Increase. Low confidence: Low confidence: Inconsistent signal Medium confidence: Increase of drought Medium confidence: Increase of drought
ECOL Dominant signal shows an increase Limited evidence due to in CMIP6 (Chapter 11 Supplementary frequency and severity based on some AGR frequency and severity based on some AGR and
in available studies based on soil limited number of Material (11.SM)) or weak (Xu et al., and ECOL drought metrics, for surface soil ECOL drought metrics, for CMIP6 surface soil
moisture models and SPEI-PM studies; one study
2019a) or insignificant signal moisture and SPEI-PM (Chapter 11 moisture, root-zone soil moisture in hydrological
(Greve et al., 2014; Trnka et al., suggests attribution of
2015b; Hanel et al., 2018; Moravec the 2017 drought event (Samaniego et al., 2018), mostly in Supplementary Material (11.SM))(Naumann et models, and SPEI-PM (Chapter 11 Supplementary
et al., 2019; Spinoni et al., 2019; to climate change due to summer season. A bit stronger signal al., 2018; Samaniego et al., 2018; Xu et al., Material (11.SM))(Naumann et al., 2018;
Padrón et al., 2020; Markonis et al., decreasing trends in soil based on SPEI-PM projections 2019a), mostly for summer season, but Samaniego et al., 2018; Xu et al., 2019a; Cook et
2021), despitesome conflicting moisture (García-Herrera (Naumann et al., 2018) inconsistent trends for CMIP6 total soil al., 2020), mostly in summer season, but
trends in some subregions et al. 2019) moisture (Chapter 11 Supplementary Material inconsistent trends for CMIP6 total soil moisture
(Spinoni et al., 2019; Padrón et al., (11.SM))(Cook et al., 2020) (Chapter 11 Supplementary Material (11.SM))
2020).
despite projected drying in substantial fraction of
domain, in particular over France (Cook et al.,
2020)
HYDR Low confidence: Weak or Low confidence: Low confidence: No or weak Medium confidence: Increase in drying, Medium confidence: Increase based on several
insignificant trends (Stahl et al., Limited evidence changes; CORDEX simulations: no mostly in western part of domain: summer lignes of evidence: Tendency towards drying but
2010; Bard et al., 2015; Caillouet et because of lack of change in most of domain, slight season surface runoff compared to pre- geographical variations (Prudhomme et al., 2014;
al., 2017; Moravec et al., 2019; studies. wetting over the Alps (Forzieri et al., industrial (Cook et al., 2020); annual discharge Giuntoli et al., 2015; Touma et al., 2015; Cook et
Vicente-Serrano et al., 2019; 2014; Touma et al., 2015; Marx et al., in substantial part of domain (Schewe et al., al., 2020)
Do Not Cite, Quote or Distribute 11-215 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
Gudmundsson et al., 2021) 2018) 2014); increase in duration and magnitude of
low flows over France, decrease in eastern part
of domain (Touma et al., 2015; Roudier et al.,
2016); CORDEX simulations. drying in
western and southeastern parts of domain, but
wetting over the Alps (Forzieri et al., 2014;
Marx et al., 2018)
Eastern MET Low confidence: Inconsistent or Low confidence: Low confidence: Inconsistent Low confidence: Inconsistent changes. Low confidence: Inconsistent changes.
Europe insignificant changes. Inconsistent Limited evidence changes. Inconsistent changes in Inconsistent changes in CDD in CMIP6 Inconsistent CDD changes in CMIP6 (Chapter 11
(EEU) or insignificant changes in CDD because of lack of CDD in CMIP6 (Chapter 11 (Chapter 11 Supplementary Material (11.SM)) Supplementary Material (11.SM)) and weak
(Khlebnikova et al., 2019b; Dunn et studies. Supplementary Material (11.SM)) and and in SPI in CMIP5 (Touma et al., 2015; Xu decrease in drying or inconsistent changes in SPI
al., 2020). No change or
insignificant changes in SPI (Stagge inSPI in CMIP5 (Touma et al., 2015; et al., 2019a) projections (Touma et al., 2015; Spinoni et al.,
et al., 2017; Caloiero et al., 2018; Xu et al., 2019a). 2020; Vicente-Serrano et al., 2020a)
Spinoni et al., 2019)
AGR Low confidence: Inconsistent or Low confidence: Low confidence based on different Low confidence based on different metrics : Low confidence: Inconsistent trends based on
ECOL weak changes (Greve et al., 2014; Limited evidence metrics: Inconsistent trends in both Inconsistent trends in both CMIP6 surface different metrics: Slight wetting or inconsistent
Spinoni et al., 2019; Padrón et al., because of lack of CMIP6 surface and total soil moisture and total soil moisture (Chapter 11 trends in total soil moisture (Chapter 11
2020) studies
(Chapter 11 Supplementary Material Supplementary Material (11.SM)); weak Supplementary Material (11.SM)); (Cook et al.,
(11.SM)); weak trends in CMIP5 soil trends in CMIP5 soil moisture (Xu et al., 2020); slight drying in surface soil moisture
moisture (Xu et al., 2019a) or SPEI- 2019a) or SPEI-PM (Naumann et al., 2018) (Chapter 11 Supplementary Material (11.SM));
PM (Naumann et al., 2018) projections (Cook et al., 2020). Increasing drying of
projections measures based on evaporative demand
(Naumann et al., 2018)
HYDR Low confidence: No enough data Low confidence: Low confidence: Limited evidence. Low confidence: Inconsistent changes. Medium confidence: Weak increase. (Forzieri
and limited studies (Gudmundsson Limited evidence One study shows lack of signal Some studies with increases in et al., 2014) [11 RCMs forced with CMIP5
et al., 2021) because of lack of (Touma et al., 2015) drought/decrease in runoff: (Forzieri et al., models and the LISFLOOD model] : Decrease
studies 2014) [11 RCMs forced with CMIP5 models in the 20 yr return level minimum flow and
and the LISFLOOD model] : Decrease in the deficit. (Cook et al., 2020) [13 CMIP6 models
20 yr return level minimum flow and deficit and SSP3-7.0. Moderate decrease (20%) of total
volumes; (Cook et al., 2020): decrease in runoff in eastern Europe during the warm
summer surface runoff in CMIP6 models. season.. (Prudhomme et al., 2014) [5 CMIP5
Some studies with no change in HYDR models and 7 global impact models. RCP8.5]
drought or runoff: (Touma et al., 2015; Small increase (10%) of dry days. (Giuntoli et
Roudier et al., 2016) [11 RCMs]: No al., 2015): Weak increase in probability of low
substantial changes in the severity of the low flow but low signal to noise ratio.
flows; (Schewe et al., 2014): No substantial
changes in the annual runoff.
Do Not Cite, Quote or Distribute 11-216 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
Northern MET Medium confidence: Decrease in Medium confidence: Medium confidence: Decrease of Medium confidence: Decrease of drought Medium confidence: Decrease of drought
Europe intensity and frequency; but Human contribution to drought frequency and severity based frequency and severity based on SPI indices frequency and severity based on SPI indices
(NEU) dependence on considered index, decrease (Gudmundsson on SPI indices (Touma et al., 2015; (Touma et al., 2015; Xu et al., 2019a), but (Touma et al., 2015; Spinoni et al., 2020;
time frame and region, including and Seneviratne, 2016). Xu et al., 2019a), but unclear sign in unclear sign in CDD (Chapter 11 Vicente-Serrano et al., 2020a) but unclear sign
negligible trends over shorter CDD (Chapter 11 Supplementary Supplementary Material (11.SM)). and drying tendency in CDD (Chapter 11
periods or some subregions Material (11.SM)). Supplementary Material (11.SM)). Same
(Orlowsky and Seneviratne, 2013; assessment for pre-industrial and recent past
Stagge et al., 2017; Spinoni et al., baselines.
2019; Dunn et al., 2020)
AGR Low confidence: Overall weak Low confidence: Low confidence: Inconsistent signal Low confidence: Inconsistent signal in Low confidence: Inconsistent signal in CMIP6
ECOL signals and signs depend on Limited evidence in CMIP6 total soil moisture at CMIP6 total soil moisture at +2°C compared to total soil moisture at +4°C compared to pre-
considered season and index (Greve because of lack of +1.5°C compared to pre-industrial pre-industrial baseline (Chapter 11 industrial baseline (Chapter 11 Supplementary
et al., 2014; Spinoni et al., 2019; studies baseline (Chapter 11 Supplementary Supplementary Material (11.SM)). Overall Material (11.SM)). Overall inconsistency of
Padrón et al., 2020; Markonis et al., Material (11.SM)). Overall inconsistency of signals between studies for signals between studies for different indices (e.g.
2021) inconsistency of signals between different indices (e.g. total soil moisture, total soil moisture, surface soil moisture, SPEI-
studies for different indices (e.g. total surface soil moisture, SPEI-PM) independently PM) independently of global warming level
soil moisture, surface soil moisture, of global warming level (Naumann et al., (Naumann et al., 2018; Xu et al., 2019a; Cook et
SPEI-PM) independently of global 2018; Xu et al., 2019a; Cook et al., 2020); but al., 2020; Vicente-Serrano et al., 2020a), but some
warming level (Naumann et al., 2018; some spatial variations in trends and stronger spatial variations in trends and stronger signals in
Xu et al., 2019a; Cook et al., 2020), signals in summer and over Scandinavia summer and over Scandinavia compared to UK
but some spatial variations in trends compared to UK (Samaniego et al., 2018). (Samaniego et al., 2018). Same assessment for
and stronger signals in summer Same assessment for pre-industrial and recent pre-industrial and recent past baseline.
(Samaniego et al., 2018). Same past baseline.
assessment for pre-industrial and
recent past baseline.
HYDR Medium confidence: Decrease in Low confidence: Low confidence: Weak and Low confidence: Inconsistent changes, Medium confidence: Weak increase in
hydrological drought for overall Limited evidence inconsistent signals. Slight increase generally with drying in ESMs (CMIP5, hydrological drought in summer but low signal-to-
region, but trends are weak, can be because of lack of in Scandinavia, slight decrease or no CMIP6) and wetting in CORDEX (Forzieri et noise ratio (Prudhomme et al., 2014; Giuntoli et
of different sign in sub-regions, and studies
change in the UK (Forzieri et al., al., 2014; Touma et al., 2015; Roudier et al., al., 2015; Touma et al., 2015; Cook et al., 2020)
are dependent on time frame
(Harrigan et al., 2018; Kay et al., 2014; Touma et al., 2015; Marx et al., 2016; Dai et al., 2018; Marx et al., 2018; Cook
2018; Barker et al., 2019; 2018) et al., 2020).
Gudmundsson et al., 2019, 2021;
Vicente-Serrano et al., 2019) Cook et al. (2020): Weak increase in
hydrological drought (decrease in runoff) in
summer in Scandinavia.
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Final Government Distribution Chapter11 IPCC AR6 WGI
Roudier et al. (2016): Decrease in magnitude
and duration of low-flows (wetting trend)
Dai et al. (2018): CMIP5, RCP4.5, (2070-
2099)-(1970-1999): slight drying trend, but
lack of model agreement.
Forzieri et al. (2014), for (2050 compared to
1961-1990 baseline), CORDEX simulations:
Decrease in magnitude of low-flow in
Scandinavia no change in UK,
Marx et al. (2018), CORDEX simulations:
slight wetting in Scandinavia
1
2
3 [START TABLE 11.19 HERE]
4
5 Table 11.19: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for temperature extremes in North America,
6 subdivided by AR6 regions. See Sections 11.9.1 and 11.9.2 for details
Detection and attribution; Projections
Region Observed trends
event attribution 1.5 °C 2 °C 4 °C
All North America Most subregions show a likely Robust evidence of a human CMIP6 models project a CMIP6 models project a CMIP6 models project a
increase in the intensity and contribution to the observed robust increase in the robust increase in the robust increase in the
frequency of hot extremes increase in the intensity and intensity and frequency of intensity and frequency of intensity and frequency of
and decrease in the intensity frequency of hot extremes TXx events and a robust TXx events and a robust TXx events and a robust
and frequency of cold and decrease in the intensity decrease in the intensity and decrease in the intensity and decrease in the intensity and
extremes and frequency of cold frequency of TNn events (Li frequency of TNn events (Li frequency of TNn events (Li
extremes (Seong et al., 2020) et al., 2020). Median increase et al., 2020). Median increase et al., 2020). Median increase
of more than 0.5°C in the 50- of more than 1°C in the 50- of more than 4.5°C in the 50-
year TXx and TNn events year TXx and TNn events year TXx and TNn events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020) level (Li et al., 2020) level (Li et al., 2020)
Very likely increase in the Human influence very likely Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contributed to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Very likely (compared with Extremely likely (compared Virtually certain (compared
intensity and frequency of frequency of hot extremes the recent past (1995-2014)) with the recent past (1995- with the recent past (1995-
cold extremes and decrease in the intensity Extremely likely (compared 2014)) 2014))
and frequency of cold with pre-industrial) Virtually certain (compared Virtually certain (compared
with pre-industrial) with pre-industrial)
extremes
Decrease in the intensity and
frequency of cold extremes: Decrease in the intensity and Decrease in the intensity and
Very likely (compared with frequency of cold extremes: frequency of cold extremes:
the recent past (1995-2014)) Extremely likely (compared Virtually certain (compared
Extremely likely (compared with the recent past (1995- with the recent past (1995-
Do Not Cite, Quote or Distribute 11-218 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
with pre-industrial) 2014)) 2014))
Virtually certain (compared Virtually certain (compared
with pre-industrial) with pre-industrial)
North Central America Significant increases in the Strong evidence of changes CMIP6 models project an CMIP6 models project a CMIP6 models project a
(NCA) intensity and frequency of hot from observations that are in increase in the intensity and robust increase in the robust increase in the
extremes and significant the direction of model frequency of TXx events and intensity and frequency of intensity and frequency of
decreases in the intensity and projected changes for the a decrease in the intensity and TXx events and a decrease in TXx events and a robust
frequency of cold extremes future. The magnitude of frequency of TNn events (Li the intensity and frequency of decrease in the intensity and
(García-Cueto et al., 2019; projected changes increases et al., 2020; Annex). Median TNn events (Li et al., 2020; frequency of TNn events (Li
Martinez-Austria and increase of more than 0.5°C Annex). Median increase of et al., 2020; Annex). Median
with global warming.
Bandala, 2017; Montero- in the 50-year TXx and TNn more than 1°C in the 50-year increase of more than 3.5°C
Martínez et al., 2018; Dunn et events compared to the 1°C TXx and TNn events in the 50-year TXx and TNn
al., 2020) warming level (Li et al., compared to the 1°C warming events compared to the 1°C
2020) and more than 1.5°C in level (Li et al., 2020) and warming level (Li et al.,
annual TXx and TNn more than 2°C in annual TXx 2020) and more than 4.5°C in
compared to pre-industrial and TNn compared to pre- annual TXx and TNn
(Annex). industrial (Annex). compared to pre-industrial
(Annex).
Additional evidence from Additional evidence from
CMIP5 and RCM simulations CMIP5 and RCM simulations Additional evidence from
for an increase in the intensity for an increase in the intensity CMIP5 and RCM simulations
and frequency of hot and frequency of hot for an increase in the intensity
extremes and decrease in the extremes and decrease in the and frequency of hot
intensity and frequency of intensity and frequency of extremes and decrease in the
cold extremes (Kharin et al., cold extremes (Kharin et al., intensity and frequency of
2013; Sillmann et al., 2013b; 2013; Sillmann et al., 2013b; cold extremes (Kharin et al.,
Alexandru, 2018; Wehner et Alexandru, 2018; Wehner et 2013; Sillmann et al., 2013b;
al., 2018b) al., 2018b) Alexandru, 2018; Wehner et
al., 2018b)
Likely increase in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the observed increase in the High confidence (compared Likely (compared with the Extremely likely (compared
intensity and frequency of intensity and frequency of hot with the recent past (1995- recent past (1995-2014)) with the recent past (1995-
cold extremes extremes and decrease in the 2014)) Very likely (compared with 2014))
intensity and frequency of Likely (compared with pre- pre-industrial) Virtually certain (compared
industrial) with pre-industrial)
cold extremes
Decrease in the intensity and
Decrease in the intensity and frequency of cold extremes: Decrease in the intensity and
frequency of cold extremes: High confidence (compared frequency of cold extremes:
Medium confidence with the recent past (1995- Very likely (compared with
(compared with the recent 2014)) the recent past (1995-2014))
past (1995-2014)) Likely (compared with pre- Extremely likely (compared
High confidence (compared industrial) with pre-industrial)
with pre-industrial)
W. North America (WNA) Significant increases in the Evidence of a human CMIP6 models project a CMIP6 models project a CMIP6 models project a
intensity and frequency of hot contribution to the observed robust increase in the robust increase in the robust increase in the
extremes and significant increase in the intensity and intensity and frequency of intensity and frequency of intensity and frequency of
decreases in the intensity and frequency of hot extremes TXx events and a decrease in TXx events and a decrease in TXx events and a robust
Do Not Cite, Quote or Distribute 11-219 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
frequency of cold extremes and decrease in the intensity the intensity and frequency of the intensity and frequency of decrease in the intensity and
(Vose et al., 2017; Dunn et and frequency of cold TNn events (Li et al., 2020; TNn events (Li et al., 2020; frequency of TNn events (Li
al., 2020) extremes (Seager et al., 2015; Annex). Median increase of Annex). Median increase of et al., 2020; Annex). Median
Angélil et al., 2017) more than 0C in the 50-year more than 1°C in the 50-year increase of more than 5°C in
TXx and TNn events TXx and TNn events the 50-year TXx and TNn
compared to the 1°C warming compared to the 1°C warming events compared to the 1°C
level (Li et al., 2020) and level (Li et al., 2020) and warming level (Li et al.,
more than 2°C in annual TXx more than 3°C in annual TXx 2020) and more than 5.5°C in
and TNn compared to pre- and TNn compared to pre- annual TXx and TNn
industrial (Annex). industrial (Annex). compared to pre-industrial
(Annex).
Additional evidence from Additional evidence from
CMIP5 and RCM simulations CMIP5 and RCM simulations Additional evidence from
for an increase in the intensity for an increase in the intensity CMIP5 and RCM simulations
and frequency of hot and frequency of hot for an increase in the intensity
extremes and decrease in the extremes and decrease in the and frequency of hot
intensity and frequency of intensity and frequency of extremes and decrease in the
cold extremes (Vose et al., cold extremes (Vose et al., intensity and frequency of
2017; Palipane and Grotjahn, 2017; Palipane and Grotjahn, cold extremes (Vose et al.,
2018; Wehner et al., 2018b) 2018; Wehner et al., 2018b) 2017; Palipane and Grotjahn,
2018; Wehner et al., 2018b)
Likely increase in the Medium confidence in a Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot human contribution to the frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the observed increase in the Likely (compared with the Very likely (compared with Virtually certain (compared
intensity and frequency of intensity and frequency of hot recent past (1995-2014)) the recent past (1995-2014)) with the recent past (1995-
cold extremes extremes and decrease in the Very likely (compared with Extremely likely (compared 2014))
intensity and frequency of pre-industrial) with pre-industrial) Virtually certain (compared
cold extremes with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Medium confidence High confidence (compared frequency of cold extremes:
(compared with the recent with the recent past (1995- Very likely (compared with
past (1995-2014)) 2014)) the recent past (1995-2014))
High confidence (compared Likely (compared with pre- Extremely likely (compared
with pre-industrial) industrial) with pre-industrial)
C. North America (CNA) Weak and inconsistent trends Evidence of a human CMIP6 models project a CMIP6 models project a CMIP6 models project a
(Dunn et al., 2020) contribution for some events robust increase in the robust increase in the robust increase in the
but cannot be generalized intensity and frequency of intensity and frequency of intensity and frequency of
TXx events and a decrease in TXx events and a decrease in TXx events and a robust
the intensity and frequency of the intensity and frequency of decrease in the intensity and
TNn events (Li et al., 2020; TNn events (Li et al., 2020; frequency of TNn events (Li
Annex). Median increase of Annex). Median increase of et al., 2020; Annex). Median
more than 0.5°C in the 50- more than 1.5°C in the 50- increase of more than 4.5°C
year TXx and TNn events year TXx and TNn events in the 50-year TXx and TNn
compared to the 1°C warming compared to the 1°C warming events compared to the 1°C
level (Li et al., 2020) and level (Li et al., 2020) and warming level (Li et al.,
more than 2°C in annual TXx more than 3°C in annual TXx 2020) and more than 5.5°C in
and TNn compared to pre- and TNn compared to pre- annual TXx and TNn
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Final Government Distribution Chapter11 IPCC AR6 WGI
industrial (Annex). industrial (Annex). compared to pre-industrial
(Annex).
Additional evidence from Additional evidence from
CMIP5 and RCM simulations CMIP5 and RCM simulations Additional evidence from
for an increase in the intensity for an increase in the intensity CMIP5 and RCM simulations
and frequency of hot and frequency of hot for an increase in the intensity
extremes and decrease in the extremes and decrease in the and frequency of hot
intensity and frequency of intensity and frequency of extremes and decrease in the
cold extremes (Vose et al., cold extremes (Vose et al., intensity and frequency of
2017; Wehner et al., 2018b) 2017; Wehner et al., 2018b) cold extremes (Vose et al.,
2017; Wehner et al., 2018b)
Low confidence Low confidence Increase in the intensity and Increase in the intensity and Increase in the intensity and
frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
Likely (compared with the Very likely (compared with Virtually certain (compared
recent past (1995-2014)) the recent past (1995-2014)) with the recent past (1995-
Very likely (compared with Extremely likely (compared 2014))
pre-industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Medium confidence High confidence (compared frequency of cold extremes:
(compared with the recent with the recent past (1995- Very likely (compared with
past (1995-2014)) 2014)) the recent past (1995-2014))
High confidence (compared Very likely (compared with Extremely likely (compared
with pre-industrial) pre-industrial) with pre-industrial)
E. North America (ENA) Weak and inconsistent trends Evidence of a human CMIP6 models project a CMIP6 models project a CMIP6 models project a
(Dunn et al., 2020) contribution for some events, robust increase in the robust increase in the robust increase in the
but cannot be generalized intensity and frequency of intensity and frequency of intensity and frequency of
TXx events and a robust TXx events and a robust TXx events and a robust
decrease in the intensity and decrease in the intensity and decrease in the intensity and
frequency of TNn events (Li frequency of TNn events (Li frequency of TNn events (Li
et al., 2020; Annex). Median et al., 2020; Annex). Median et al., 2020; Annex). Median
increase of more than 0.5°C increase of more than 1.5°C increase of more than 5°C in
in the 50-year TXx and TNn in the 50-year TXx and TNn the 50-year TXx and TNn
events compared to the 1°C events compared to the 1°C events compared to the 1°C
warming level (Li et al., warming level (Li et al., warming level (Li et al.,
2020) and more than 1.5°C in 2020) and more than 2.5°C in 2020) and more than 5.5°C in
annual TXx and TNn annual TXx and TNn annual TXx and TNn
compared to pre-industrial compared to pre-industrial compared to pre-industrial
(Annex). (Annex). (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations CMIP5 and RCM simulations CMIP5 and RCM simulations
for an increase in the intensity for an increase in the intensity for an increase in the intensity
and frequency of hot and frequency of hot and frequency of hot
extremes and decrease in the extremes and decrease in the extremes and decrease in the
intensity and frequency of intensity and frequency of intensity and frequency of
cold extremes (Vose et al., cold extremes (Vose et al., cold extremes (Vose et al.,
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Final Government Distribution Chapter11 IPCC AR6 WGI
2017; Wehner et al., 2018b; 2017; Wehner et al., 2018b; 2017; Wehner et al., 2018b;
Zhang et al., 2019d). Zhang et al., 2019d). Zhang et al., 2019d).
Low confidence Low confidence Increase in the intensity and Increase in the intensity and Increase in the intensity and
frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
Likely (compared with the Very likely (compared with Virtually certain (compared
recent past (1995-2014)) the recent past (1995-2014)) with the recent past (1995-
Very likely (compared with Extremely likely (compared 2014))
pre-industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the Very likely (compared with frequency of cold extremes:
recent past (1995-2014)) the recent past (1995-2014)) Virtually certain (compared
Very likely (compared with Extremely likely (compared with the recent past (1995-
pre-industrial) with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
N. E. North America (NEN) Significant increases in the Robust evidence of a human CMIP6 models project a CMIP6 models project a CMIP6 models project a
intensity and frequency of hot contribution to the observed robust increase in the robust increase in the robust increase in the
extremes and significant increase in the intensity and intensity and frequency of intensity and frequency of intensity and frequency of
decreases in the intensity and frequency of hot extremes TXx events and a robust TXx events and a robust TXx events and a robust
frequency of cold extremes and decrease in the intensity decrease in the intensity and decrease in the intensity and decrease in the intensity and
(Vincent et al., 2018; Zhang and frequency of cold frequency of TNn events (Li frequency of TNn events (Li frequency of TNn events (Li
et al., 2019c; Dunn et al., extremes (Wan et al., 2019) et al., 2020; Annex). Median et al., 2020; Annex). Median et al., 2020; Annex). Median
2020) increase of more than 0.5°C increase of more than 1.5°C increase of more than 5°C in
in the 50-year TXx and TNn in the 50-year TXx and TNn the 50-year TXx and TNn
events compared to the 1°C events compared to the 1°C events compared to the 1°C
warming level (Li et al., warming level (Li et al., warming level (Li et al.,
2020) and more than 2°C in 2020) and more than 2.5°C in 2020) and more than 5.5°C in
annual TXx and TNn annual TXx and TNn annual TXx and TNn
compared to pre-industrial compared to pre-industrial compared to pre-industrial
(Annex). (Annex). (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations CMIP5 and RCM simulations CMIP5 and RCM simulations
for an increase in the intensity for an increase in the intensity for an increase in the intensity
and frequency of hot and frequency of hot and frequency of hot
extremes and decrease in the extremes and decrease in the extremes and decrease in the
intensity and frequency of intensity and frequency of intensity and frequency of
cold extremes (Li et al., cold extremes (Li et al., cold extremes (Li et al.,
2018d; Zhang et al., 2019d) 2018d; Zhang et al., 2019d) 2018d; Zhang et al., 2019d)
Very likely increase in the High confidence in a human Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contribution to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the Very likely (compared with Virtually certain (compared
intensity and frequency of frequency of hot extremes recent past (1995-2014)) the recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with Extremely likely (compared 2014))
and frequency of cold pre-industrial) with pre-industrial) Virtually certain (compared
extremes with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
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Final Government Distribution Chapter11 IPCC AR6 WGI
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the Very likely (compared with frequency of cold extremes:
recent past (1995-2014)) the recent past (1995-2014)) Virtually certain (compared
Very likely (compared with Extremely likely (compared with the recent past (1995-
pre-industrial) with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
N. W. North America (NWN) Significant increases in the Robust evidence of a human CMIP6 models project a CMIP6 models project a CMIP6 models project a
intensity and frequency of hot contribution to the observed robust increase in the robust increase in the robust increase in the
extremes and significant increase in the intensity and intensity and frequency of intensity and frequency of intensity and frequency of
decreases in the intensity and frequency of hot extremes TXx events and a robust TXx events and a robust TXx events and a robust
frequency of cold extremes and decrease in the intensity decrease in the intensity and decrease in the intensity and decrease in the intensity and
(Vincent et al., 2018; Zhang and frequency of cold frequency of TNn events (Li frequency of TNn events (Li frequency of TNn events (Li
et al., 2019c; Dunn et al., extremes (Wan et al., 2019) et al., 2020; Annex). Median et al., 2020; Annex). Median et al., 2020; Annex). Median
2020) increase of more than 0.5°C increase of more than 1°C in increase of more than 4°C in
in the 50-year TXx and TNn the 50-year TXx and TNn the 50-year TXx and TNn
events compared to the 1°C events compared to the 1°C events compared to the 1°C
warming level (Li et al., warming level (Li et al., warming level (Li et al.,
2020) and more than 1.5°C in 2020) and more than 2.5°C in 2020) and more than 5°C in
annual TXx and TNn annual TXx and TNn annual TXx and TNn
compared to pre-industrial compared to pre-industrial compared to pre-industrial
(Annex). (Annex). (Annex).
Additional evidence from Additional evidence from Additional evidence from
CMIP5 and RCM simulations CMIP5 and RCM simulations CMIP5 and RCM simulations
for an increase in the intensity for an increase in the intensity for an increase in the intensity
and frequency of hot and frequency of hot and frequency of hot
extremes and decrease in the extremes and decrease in the extremes and decrease in the
intensity and frequency of intensity and frequency of intensity and frequency of
cold extremes (Bennett and cold extremes (Bennett and cold extremes (Bennett and
Walsh, 2015; Li et al., 2018d; Walsh, 2015; Li et al., 2018d; Walsh, 2015; Li et al., 2018d;
Zhang et al., 2019d). Zhang et al., 2019d). Zhang et al., 2019d).
Very likely increase in the High confidence in a human Increase in the intensity and Increase in the intensity and Increase in the intensity and
intensity and frequency of hot contribution to the observed frequency of hot extremes: frequency of hot extremes: frequency of hot extremes:
extremes and decrease in the increase in the intensity and Likely (compared with the Very likely (compared with Virtually certain (compared
intensity and frequency of frequency of hot extremes recent past (1995-2014)) the recent past (1995-2014)) with the recent past (1995-
cold extremes and decrease in the intensity Very likely (compared with Extremely likely (compared 2014))
and frequency of cold pre-industrial) with pre-industrial) Virtually certain (compared
extremes with pre-industrial)
Decrease in the intensity and Decrease in the intensity and
frequency of cold extremes: frequency of cold extremes: Decrease in the intensity and
Likely (compared with the Very likely (compared with frequency of cold extremes:
recent past (1995-2014)) the recent past (1995-2014)) Virtually certain (compared
Very likely (compared with Extremely likely (compared with the recent past (1995-
pre-industrial) with pre-industrial) 2014))
Virtually certain (compared
with pre-industrial)
1
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Final Government Distribution Chapter11 IPCC AR6 WGI
1 [END TABLE 11.19 HERE]
2
3
4 [START TABLE 11.20 HERE]
5
6 Table 11.20: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for heavy precipitation in North America,
7 subdivided by AR6 regions. See Sections 11.9.1 and 11.9.3 for details
Detection and attribution; Projections
Region Observed trends
event attribution 1.5 °C 2 °C 4 °C
All North America Significant intensification of Robust evidence of a human CMIP6 models project a CMIP6 models project a CMIP6 models project a
heavy precipitation (Sun et contribution to the observed robust increase in the robust increase in the robust increase in the
al., 2020; Dunn et al., 2020) intensification of heavy intensity and frequency of intensity and frequency of intensity and frequency of
precipitation (Kirchmeier- heavy precipitation (Li et al., heavy precipitation (Li et al., heavy precipitation (Li et al.,
Young and Zhang, 2020; Paik 2020a). Median increase of 2020a). Median increase of 2020a). Median increase of
et al., 2020) more than 2% in the 50-year more than 6% in the 50-year more than 15% in the 50-year
Rx1day and Rx5day events Rx1day and Rx5day events Rx1day and Rx5day events
compared to the 1°C warming compared to the 1°C warming compared to the 1°C warming
level (Li et al., 2020a) level (Li et al., 2020a) level (Li et al., 2020a)
Likely intensification of heavy Human influence likely Intensification of heavy Intensification of heavy Intensification of heavy
precipitation contributed to the observed precipitation: precipitation: precipitation:
intensification of heavy Likely (compared with the Very likely (compared with Virtually certain (compared
precipitation recent past (1995-2014)) the recent past (1995-2014)) with the recent past (1995-
Very likely (compared with Extremely likely (compared 2014))
pre-industrial) with pre-industrial) Virtually certain (compared
with pre-industrial)
North Central America Trends are generally not Disagreement among studies CMIP6 models project an CMIP6 models project an CMIP6 models project a
(NCA) significant (Sun et al., 2020; (Eden et al., 2016; Pall et al., increase in the intensity and increase in the intensity and robust increase in the
Dunn et al., 2020; Donat et 2017; Hoerling et al., 2014) frequency of heavy frequency of heavy intensity and frequency of
al., 2016; García-Cueto et al., precipitation (Li et al., 2020; precipitation (Li et al., 2020; heavy precipitation (Li et al.,
2019) Annex). Median increase of Annex). Median increase of 2020; Annex). Median
more than 2% in the 50-year more than 4% in the 50-year increase of more than 15% in
Rx1day and Rx5day events Rx1day and Rx5day events the 50-year Rx1day and
compared to the 1°C warming compared to the 1°C warming Rx5day events compared to
level (Li et al., 2020a) and level (Li et al., 2020a) and the 1°C warming level (Li et
more than 2% in annual more than 4% in annual al., 2020a) and more than
Rx1day and Rx5day and 0% Rx1day and Rx5day and 0% 10% in annual Rx1day and
in annual Rx30day compared in annual Rx30day compared Rx5day and 2% in annual
to pre-industrial (Annex). to pre-industrial (Annex). Rx30day compared to pre-
industrial (Annex).
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Medium confidence High confidence (compared Very likely (compared with
(compared with the recent with the recent past (1995- the recent past (1995-2014))
past (1995-2014)) 2014)) Extremely likely (compared
High confidence (compared Likely (compared with pre- with pre-industrial)
Do Not Cite, Quote or Distribute 11-224 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
with pre-industrial) industrial)
W. North America (WNA) Lack of agreement on the Evidence of a human CMIP6 models project CMIP6 models project an CMIP6 models project a
evidence of trends (Sun et al., contribution for some events inconsistent changes in the increase in the intensity and robust increase in the
2020; Dunn et al., 2020; (Easterling et al., 2017; region (Li et al., 2020a) frequency of heavy intensity and frequency of
Easterling et al. 2017; Wu Kirchmeier-Young and precipitation (Li et al., 2020; heavy precipitation (Li et al.,
2015) Zhang, 2020), but cannot be Annex). Median increase of 2020; Annex). Median
generalized more than 2% in the 50-year increase of more than 10% in
Rx1day and Rx5day events the 50-year Rx1day and
compared to the 1°C warming Rx5day events compared to
level (Li et al., 2020a) and the 1°C warming level (Li et
more than 6% in annual al., 2020a) and more than
Rx1day and Rx5day and 4% 10% in annual Rx1day,
in annual Rx30day compared Rx5day, and Rx30day
to pre-industrial (Annex). compared to pre-industrial
(Annex).
Additional evidence from
CMIP5 and RCM simulations Additional evidence from
for an increase in the intensity CMIP5 and RCM simulations
of heavy precipitation for an increase in the intensity
(Easterling et al., 2017) of heavy precipitation
(Easterling et al., 2017)
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
Low confidence (compared Medium confidence Likely (compared with the
with the recent past (1995- (compared with the recent recent past (1995-2014))
2014)) past (1995-2014)) Very likely (compared with
Medium confidence High confidence (compared pre-industrial)
(compared with pre- with pre-industrial)
industrial)
C. North America (CNA) Significant intensification of Evidence of a human CMIP6 models project an CMIP6 models project an CMIP6 models project a
heavy precipitation (Dunn et contribution to the observed increase in the intensity and increase in the intensity and robust increase in the
al., 2020; Easterling et al., intensification of heavy frequency of heavy frequency of heavy intensity and frequency of
2017; Wu, 2015; Emanuel, precipitation (Easterling et al., precipitation (Li et al., 2020; precipitation (Li et al., 2020; heavy precipitation (Li et al.,
2017; Risser and Wehner, 2017; Kirchmeier-Young and Annex). Median increase of Annex). Median increase of 2020; Annex). Median
2017; Trenberth et al., 2018; Zhang, 2020; Emanuel, 2017; more than 2% in the 50-year more than 4% in the 50-year increase of more than 10% in
van Oldenborgh et al., 2017; Risser and Wehner, 2017; Rx1day and Rx5day events Rx1day and Rx5day events the 50-year Rx1day and
Wang et al., 2018). Trenberth et al., 2018; van compared to the 1°C warming compared to the 1°C warming Rx5day events compared to
Oldenborgh et al., 2017; level (Li et al., 2020a) and level (Li et al., 2020a) and the 1°C warming level (Li et
Wang et al., 2018) more than 4% in annual more than 6% in annual al., 2020a) and more than
Rx1day and Rx5day and 2% Rx1day, Rx5day, and 10% in annual Rx1day,
in annual Rx30day compared Rx30day compared to pre- Rx5day, and Rx30day
to pre-industrial (Annex). industrial (Annex). compared to pre-industrial
(Annex).
Additional evidence from Additional evidence from
CMIP5 and RCM simulations CMIP5 and RCM simulations Additional evidence from
for an increase in the intensity for an increase in the intensity CMIP5 and RCM simulations
of heavy precipitation of heavy precipitation for an increase in the intensity
Do Not Cite, Quote or Distribute 11-225 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
(Easterling et al., 2017) (Easterling et al., 2017) of heavy precipitation
(Easterling et al., 2017;
Knutson et al., 2015; Kossin
et al., 2017)
High confidence in the Medium confidence in a Intensification of heavy Intensification of heavy Intensification of heavy
intensificationof heavy human contribution to the precipitation: precipitation: precipitation:
precipitation intensification of heavy Medium confidence High confidence (compared Very likely (compared with
precipitation. (compared with the recent with the recent past (1995- the recent past (1995-2014))
past (1995-2014)) 2014)) Extremely likely (compared
High confidence (compared Likely (compared with pre- with pre-industrial)
with pre-industrial) industrial)
E. North America (ENA) Significant intensification of Evidence of a human CMIP6 models project an CMIP6 models project an CMIP6 models project a
heavy precipitation (Sun et contribution for some events increase in the intensity and increase in the intensity and robust increase in the
al., 2020; Dunn et al., 2020; (Easterling et al., 2017; frequency of heavy frequency of heavy intensity and frequency of
Easterling et al., 2017; Wu, Teufel et al., 2019; precipitation (Li et al., 2020; precipitation (Li et al., 2020; heavy precipitation (Li et al.,
2015; Emanuel, 2017; Risser Kirchmeier-Young and Annex). Median increase of Annex). Median increase of 2020; Annex). Median
and Wehner, 2017; Trenberth Zhang, 2020), but cannot be more than 2% in the 50-year more than 4% in the 50-year increase of more than 15% in
et al., 2018; van Oldenborgh generalized Rx1day and Rx5day events Rx1day and Rx5day events the 50-year Rx1day and
et al., 2017; Wang et al., compared to the 1°C warming compared to the 1°C warming Rx5day events compared to
2018), but a lack of a level (Li et al., 2020a) and level (Li et al., 2020a) and the 1°C warming level (Li et
significant trend over Canada more than 6% in annual more than 8% in annual al., 2020a) and more than
(Shephard et al., 2014; Mekis Rx1day and Rx5day and 4% Rx1day and Rx5day and 6% 15% in annual Rx1day and
et al., 2015; Vincent et al., in annual Rx30day compared in annual Rx30day compared Rx5day and 10% in annual
2018) to pre-industrial (Annex). to pre-industrial (Annex). Rx30day compared to pre-
industrial (Annex).
Additional evidence from Additional evidence from
CMIP5 and RCM simulations CMIP5 and RCM simulations Additional evidence from
for an increase in the intensity for an increase in the intensity CMIP5 and RCM simulations
of heavy precipitation (Zhang of heavy precipitation (Zhang for an increase in the intensity
et al., 2019; Easterling et al., et al., 2019; Easterling et al., of heavy precipitation (Zhang
2017) 2017) et al., 2019; Easterling et al.,
2017; Knutson et al., 2015;
Kossin et al., 2017)
High confidence in the Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
intensification of heavy precipitation: precipitation: precipitation:
precipitation Medium confidence High confidence (compared Very likely (compared with
(compared with the recent with the recent past (1995- the recent past (1995-2014))
past (1995-2014)) 2014)) Extremely likely (compared
High confidence (compared Likely (compared with pre- with pre-industrial)
with pre-industrial) industrial)
N. E. North America (NEN) Limited evidence (Shephard Evidence of a human CMIP6 models project an CMIP6 models project a CMIP6 models project a
et al., 2014; Mekis et al., contribution for some events increase in the intensity and robust increase in the robust increase in the
2015; Vincent et al., 2018) (Szeto et al., 2015), but frequency of heavy intensity and frequency of intensity and frequency of
cannot be generalized precipitation (Li et al., 2020; heavy precipitation (Li et al., heavy precipitation (Li et al.,
Annex). Median increase of 2020; Annex). Median 2020; Annex). Median
more than 2% in the 50-year increase of more than 6% in increase of more than 20% in
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Final Government Distribution Chapter11 IPCC AR6 WGI
Rx1day and Rx5day events the 50-year Rx1day and the 50-year Rx1day and
compared to the 1°C warming Rx5day events compared to Rx5day events compared to
level (Li et al., 2020a) and the 1°C warming level (Li et the 1°C warming level (Li et
more than 8% in annual al., 2020a) and more than al., 2020a) and more than
Rx1day and Rx5day and 6% 10% in annual Rx1day and 20% in annual Rx1day and
in annual Rx30day compared Rx5day and 8% in annual Rx5day and 15% in annual
to pre-industrial (Annex). Rx30day compared to pre- Rx30day compared to pre-
industrial (Annex). industrial (Annex).
Additional evidence from
CMIP5 and RCM simulations Additional evidence from Additional evidence from
for an increase in the intensity CMIP5 and RCM simulations CMIP5 and RCM simulations
of heavy precipitation (Zhang for an increase in the intensity for an increase in the intensity
et al., 2019d) of heavy precipitation (Zhang of heavy precipitation (Zhang
et al., 2019d) et al., 2019d)
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
High confidence (compared Likely (compared with the Extremely likely (compared
with the recent past (1995- recent past (1995-2014)) with the recent past (1995-
2014)) Very likely (compared with 2014))
Likely (compared with pre- pre-industrial) Virtually certain (compared
industrial) with pre-industrial)
N. W. North America (NWN) Lack of agreement on the Evidence of a human CMIP6 models project an CMIP6 models project a CMIP6 models project a
evidence of trends (Sun et al., contribution for some events increase in the intensity and robust increase in the robust increase in the
2020; Dunn et al., 2020; (Teufel et al., 2017; frequency of heavy intensity and frequency of intensity and frequency of
Mekis et al., 2015; Shephard Kirchmeier-Young and precipitation (Li et al., 2020; heavy precipitation (Li et al., heavy precipitation (Li et al.,
et al., 2014; Vincent et al., Zhang, 2020), but cannot be Annex). Median increase of 2020; Annex). Median 2020; Annex). Median
2018) generalized more than 2% in the 50-year increase of more than 6% in increase of more than 20% in
Rx1day and Rx5day events the 50-year Rx1day and the 50-year Rx1day and
compared to the 1°C warming Rx5day events compared to Rx5day events compared to
level (Li et al., 2020a) and the 1°C warming level (Li et the 1°C warming level (Li et
more than 6% in annual al., 2020a) and more than al., 2020a) and more than
Rx1day, Rx5day, and 10% in annual Rx1day, 20% in annual Rx1day,
Rx30day compared to pre- Rx5day, and Rx30day Rx5day, and Rx30day
industrial (Annex). compared to pre-industrial compared to pre-industrial
(Annex). (Annex).
Additional evidence from
CMIP5 and RCM simulations Additional evidence from Additional evidence from
for an increase in the intensity CMIP5 and RCM simulations CMIP5 and RCM simulations
of heavy precipitation for an increase in the intensity for an increase in the intensity
(Bennett and Walsh, 2015; of heavy precipitation of heavy precipitation
Zhang et al., 2019d) (Bennett and Walsh, 2015; (Bennett and Walsh, 2015;
Zhang et al., 2019d) Zhang et al., 2019d)
Low confidence Low confidence Intensification of heavy Intensification of heavy Intensification of heavy
precipitation: precipitation: precipitation:
High confidence (compared Likely (compared with the Extremely likely (compared
with the recent past (1995- recent past (1995-2014)) with the recent past (1995-
2014)) Very likely (compared with 2014))
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Final Government Distribution Chapter11 IPCC AR6 WGI
Likely (compared with pre- pre-industrial) Virtually certain (compared
industrial) with pre-industrial)
1
2 [END TABLE 11.20 HERE]
3
4
5
6 [START TABLE 11.21 HERE]
7
8 Table 11.21: Observed trends, human contribution to observed trends, and projected changes at 1.5°C, 2°C and 4°C of global warming for meteorological droughts (MET),
9 agricultural and ecological droughts (AGR/ECOL), and hydrological droughts (HYDR) in North America, subdivided by AR6 regions. See Sections 11.9.1 and 11.9.4
10 for details.
Region anddrought Observed trends Human contribution Projections
types +1.5 °C +2 °C +4 °C
North MET Low confidence: Low confidence: No Low confidence: Limited Medium confidence: Increase in High confidence: Increase in
Central Inconsistent changes in the signal in precipitation evidence. Evidence suggests drought duration(Xu et al., 2019a; meteorological drought severity in the
America duration and frequency of (Funk et al., 2014; Swain tendency towards drying (Xu et Spinoni et al., 2020)(Chapter 11 majority of models (Sillmann et al.,
droughts, (Spinoni et al., et al., 2014; Wang and al., 2019a)(Chapter 11 Supplementary Material (11.SM)). 2013b; Touma et al., 2015; Escalante-
(NCA) 2019; Dunn et al., 2020). Schubert, 2014) Supplementary Material Sandoval and Nuñez-Garcia, 2017;
(11.SM)). Xu et al. (2019): Strong drying signal Spinoni et al., 2020)(Chapter 11
for meteorological drought duration Supplementary Material (11.SM)).
using SPI at 2°C compared to recent
past.
Spinoni et al. (2020): for RCP4.5
compared to recent past: SPI-based
drying trends in CORDEX GCMs, but
inconsistent signals in CORDEX
RCMs.
AGR Low evidence: No signal in Low confidence: Low evidence: Mixed signal Medium confidence: Increase of Likely: Increase of drought severity.
ECOL the duration and severity of Limited evidence between the different drought drought severity. This is consistent This is consistent between the different
droughts based on soil metrics including total column between the different drought metrics drought metrics including total column
moisture, PDSI and SPEI and soil moisture, (Chapter 11 including total column soil moisture, soil moisture, (Chapter 11
conflicting trend depending Supplementary Material (Chapter 11 Supplementary Material Supplementary Material (11.SM)),
of the subregion (Greve et (11.SM)), surface soil moisture (11.SM)), surface soil moisture (Xu et surface soil moisture (Dai et al., 2018;
al., 2014; Dai and Zhao, (Xu et al., 2019a) and a weak al., 2019a) and SPEI-PM (Naumann Lu et al., 2019), PDSI (Dai et al., 2018)
2017; Spinoni et al., 2019; drying by SPEI-PM (Naumann et al., 2018; Gu et al., 2020). and SPEI-PM (Cook et al., 2014b;
Padrón et al., 2020) et al., 2018; Gu et al., 2020). Vicente-Serrano et al., 2020a).
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited evidence . Low confidence: Mixed signal among
evidence Limited evidence evidence. One study shows Inconsistent trends in available studies studies (Prudhomme et al., 2014;
inconsistent trends(Touma et (Touma et al., 2015; Cook et al., Giuntoli et al., 2015; Touma et al., 2015;
al., 2015) 2020; Zhai et al., 2020b) Cook et al., 2020), but slight stronger
Do Not Cite, Quote or Distribute 11-228 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
tendency towards drying.
W. North MET Low confidence: Low confidence: Low confidence: Limited Low confidence: Limited evidence Low confidence: Mixed signal among
America Inconsistent trends Limited evidence evidence and inconsistent and inconsistent trends depending models, seasons, and studies (Swain
(WNA) depending on subregion trends depending on models on models and seasons (Swain and and Hayhoe, 2015; Touma et al., 2015;
(Swain and Hayhoe, 2015; and seasons (Swain and Hayhoe, 2015; Xu et al., 2019a; Spinoni et al., 2020)(Chapter 11
Wehner et al., 2017; Spinoni Hayhoe, 2015; Xu et al., Spinoni et al., 2020). Supplementary Material (11.SM)),
et al., 2019; Dunn et al., 2019a)(Chapter 11 with tendency towards drying in the
2020). Supplementary Material spring and wetting in summer (Swain
(11.SM)) and Hayhoe, 2015).
AGR Medium confidence: Medium confidence: Low evidence: Inconsistent Medium confidence: Increase of Medium confidence: Increase of
ECOL Increase. Dominant increase Human contribution to signal between models, with drought severity. There are drought severity. There are differences
but some inconsistent trends observed trend. weak tendency to increased differences depending on metrics depending onmetrics and models, with
based on soil moisture, drying in total and surface and models, with weak median weak drying in total column soil
water-balance estimates, Williams et al. (2020) soil moisture (Xu et al., drying and substantial intermodel moisture (Cook et al., 2020)(Chapter
PDSI and SPEI, but some concluded human- 2019a)(Chapter 11 spread for total soil moisture (Cook 11 Supplementary Material (11.SM)),
inconsistent trends depending induced climate change Supplementary Material et al., 2020)(Chapter 11 and substantial drying with surface soil
study, index and the contributed to the strong (11.SM)) and the SPEI-PM Supplementary Material (11.SM)) moisture (Dai et al., 2018; Lu et al.,
subregion (Greve et al., soil moisture deficits (Naumann et al., 2018; Gu et and larger drying for surface soil 2019; Cook et al., 2020)(Chapter 11
2014; Griffin and recorded in the last two al., 2020). Weak soil moisture (Xu et al., 2019a; Cook et Supplementary Material (11.SM)),
Anchukaitis, 2014; Williams decades in western North moisture drying projection al., 2020)(Chapter 11 PDSI (Dai et al., 2018) and SPEI-PM
et al., 2015, 2020; America through VPD for California (Louise et al., Supplementary Material (11.SM)) (Cook et al., 2014b; Vicente-Serrano et
Ahmadalipour and (and AED) increases 2018) and SPEI-PM (Naumann et al., al., 2020a).
Moradkhani, 2017; Dai and associated with higher 2018; Gu et al., 2020). Stronger soil
Zhao, 2017; Spinoni et al., air temperatures and moisture drying in southern part of
2019; Padrón et al., 2020) lower air humidity. domain (Cook et al., 2020).
Williams et al. (2015)
and Griffin and
Anchukaitis (2014)
concluded that increased
AED has had an
increased contribution
to drought severity over
the last decades, and
played a dominant role
in the intensification of
the 2012-2014 drought
in California
HYDR Low confidence: Mixed Low confidence: Low confidence: Limited Medium confidence: Increase in Medium confidence: Increase in
signal between different time Mixed signal for overall evidence. One study shows hydrological drought (more intense hydrological droughts (Prudhomme et
frames and subregions region in observations. drying (Touma et al., 2015) low flows, less runoff and more al., 2014; Giuntoli et al., 2015; Touma
(Gudmundsson et al., 2019, But evidence that frequent hydrological droughts) et al., 2015; Cook et al., 2020)
2021; Poshtiri and Pal, 2016; temperature increase has (Touma et al., 2015; Cook et al., Particularly strong evidence of
Dudley et al., 2020). Strong been the main driver of 2020; Zhai et al., 2020b) increasing hydrological droughts in
spatial variability in the increased hydrological Particularly strong evidence of regions dependent on snow pack
recent trends of low flows in drought in California and increasing hydrological droughts in reservoirs (Wehner et al., 2017;
the region (Poshtiri and Pal, in the Colorado basin regions dependent on snow pack Ackerly et al., 2018; Rhoades et al.,
Do Not Cite, Quote or Distribute 11-229 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
2016) but dominant increase (Milly and Dunne, 2020; reservoirs (Wehner et al., 2017; 2018)
of hydrological drought in Shukla et al., 2015; Xiao Ackerly et al., 2018; Rhoades et al.,
California and in the et al., 2018; Udall and 2018)
Colorado basin (Xiao et al., Overpeck, 2017).
2018b; Milly and Dunne,
2020).
C. North MET Medium confidence: Low confidence: Low confidence: Limited Low confidence: Mixed signal Low confidence: Mixed signal among
America Decrease in the duration and Limited evidence evidence and inconsistent among different models (Sillmann et different models (Sillmann et al., 2013b;
(CNA) frequency of meteorological (Rupp et al., 2013; trends (Xu et al., al., 2013b; Spinoni et al., Touma et al., 2015; Spinoni et al.,
droughts, (Wehner et al., Easterling et al., 2017) 2019a)(Chapter 11 2020)(Chapter 11 Supplementary 2020)(Chapter 11 Supplementary
2017; Spinoni et al., 2019; Supplementary Material Material (11.SM)). Material (11.SM)); drying trend in spring
Dunn et al., 2020). (11.SM)). and summer (Swain and Hayhoe, 2015).
AGR Low confidence: Mixed Low confidence: Medium confidence: Increase Medium confidence: Increase in High confidence: Increase of drought
ECOL signal based on soil Limited evidence. in drought. Dominant signal drought severity or frequency. severity. Changes are consistent between
moisture, water-balance Human influence on shows drought increase based Changes are consistent between different drought metrics including total
estimates, PDSI and SPEI surface soil moisture ontotal and surface soil different drought metrics including column soil moisture, (Chapter 11
and conflicting trend deficits due to increased moisture (Xu et al., total column soil moisture, (Chapter Supplementary Material (11.SM)) (Cook
depending of the subregion evapotranspiration 2019a)(Chapter 11 11 Supplementary Material et al., 2020), surface soil moisture (Dai et
(Greve et al., 2014; Dai and caused by higher Supplementary Material (11.SM))(Cook et al., 2020), surface al., 2018; Lu et al., 2019; Cook et al.,
Zhao, 2017; Seager et al., temperatures. (Easterling (11.SM)) and SPEI-PM soil moisture (Xu et al., 2019a) and 2020), PDSI (Dai et al., 2018), and SPEI-
2019; Spinoni et al., 2019; et al., 2017) (Naumann et al., 2018; Gu et SPEI-PM (Naumann et al., 2018; Gu PM (Cook et al., 2014b; Feng et al.,
Padrón et al., 2020). al., 2020). et al., 2020). 2017; Vicente-Serrano et al., 2020a).
HYDR Low confidence: Mixed Low confidence: Low confidence: Limited Low confidence: Limited evidence Low confidence: Mixed signal among
signal. No signal in changes Inconsistent trends in evidence. One study shows and inconsistent trends (Touma et studies (Prudhomme et al., 2014;
(Gudmundsson et al., 2021; observations. Two drying (Touma et al., 2015) al., 2015; Cook et al., 2020; Zhai et Giuntoli et al., 2015; Touma et al., 2015;
Mo and Lettenmaier, 2018; studies suggest that al., 2020b). Cook et al., 2020)
Dudley et al., 2020). Poshtiri emperature increase has
and Pal (2016) show strong been the main driver of
spatial variability in the increased hydrological
recent trends of low flows drought in the Missouri
although there is an increase basin (Martin et al.,
of hydrological droughts in 2020; Woodhouse and
the Missouri (Martin et al., Wise, 2020).
2020; Woodhouse and Wise,
2020) and in the Colorado
basins (Xiao et al., 2018b;
Milly and Dunne, 2020)
Wetting trend in (Dai and
Zhao, 2017)
E. North MET Low confidence: Low confidence: Low confidence: Limited Low confidence: Limited evidence Medium confidence: Increase in
America Inconsistent trends Limited evidence evidence and inconsistent (Xu et al., 2019a; Spinoni et al., drought severity in the majority of
(ENA) depending on the region (Easterling et al., 2017) trends (Xu et al., 2019a) 2020)(Chapter 11 Supplementary models, but weaker or inconsistent trends
(Wehner et al., 2017; Spinoni (Chapter 11 Supplementary Material (11.SM)). in part of region (Sillmann et al., 2013b;
et al., 2019; Dunn et al., Material (11.SM)). Touma et al., 2015; Spinoni et al.,
2020). 2020)(Chapter 11 Supplementary
Material (11.SM)).
AGR Low confidence: Mixed Low confidence: Low confidence: Low confidence: Inconsistent Medium confidence: Increase of
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Final Government Distribution Chapter11 IPCC AR6 WGI
ECOL signal. Inconsistent trends Limited evidence. Inconsistent trends between trends between models, metrics and drought severity. Consistent signal
depending on metric, Human influence on models, metrics and studies studies based on total and surface between different drought metrics
subregion, time frame and surface soil moisture based on total and surface soil moisture (Xu et al., 2019a; including total column soil moisture,
studies, based on soil deficits due to increased soil moisture (Xu et al., Cook et al., 2020)(Chapter 11 (Chapter 11 Supplementary Material
moisture, water-balance evapotranspiration 2019a)(Chapter 11 Supplementary Material (11.SM)), (11.SM)) (Cook et al., 2020), surface
estimates, PDSI, and SPEI caused by higher Supplementary Material and SPEI-PM (Naumann et al., soil moisture (Dai et al., 2018; Lu et
(Greve et al., 2014; Dai and temperatures. (Easterling (11.SM)) and SPEI-PM 2018; Gu et al., 2020), but with al., 2019), PDSI (Dai et al., 2018) and
Zhao, 2017; Park Williams et et al., 2017) (Naumann et al., 2018; Gu et stronger tendency towards drying. SPEI-PM (Cook et al., 2014b; Vicente-
al., 2017; Spinoni et al., al., 2020). Serrano et al., 2020a).
2019; Padrón et al., 2020).
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited evidence Low confidence: Mixed signal among
evidence. Decrease in low Limited evidence evidence. One study shows and inconsistent trends (Touma et models and studies (Prudhomme et al.,
flows from 1971-2020, but lack of signal (Touma et al., al., 2015; Cook et al., 2020; Zhai et 2014; Giuntoli et al., 2015; Touma et
not since 1950 2015) al., 2020b) al., 2015; Cook et al., 2020)
(Gudmundsson et al., 2019,
2021). Poshtiri and Pal,
(2016) and Dudley et al.,
(2020) show strong spatial
variability in the recent
trends of low flows in the
region.
N. E. MET Low confidence: No or Low confidence: Low confidence: Limited Medium confidence: Decrease in Medium confidence: Decrease in
North limited signal in duration Limited evidence evidence. Available evidence meteorological drought (Sillmann et meteorological drought (Touma et al.,
America and frequency of droughts suggest decrease in al., 2013b; Xu et al., 2019a; Spinoni 2015; Spinoni et al., 2020; Vicente-
(NEN) (Bonsal et al., 2019; Dunn et meteorological drought (Xu et et al., 2020)(Chapter 11 Serrano et al., 2020a)(Chapter 11
al., 2020) al., 2019a)(Chapter 11 Supplementary Material (11.SM)). Supplementary Material (11.SM)).
Supplementary Material
(11.SM)).
AGR Low confidence: Mixed Low confidence: Low confidence: Mixed signal Low confidence: Mixed signal Low confidence: Mixed signal between
ECOL signal between different Limited evidence between different models and between different models and models and different drought metrics,
drought metrics and strong metrics. Substantial intermodal drought metrics. Substantial including total column soil moisture,
spatial differences (Greve et variations and weak drying intermodel spread for total column which shows inconsistent changes
al., 2014; Dai and Zhao, trend in soil moisture(Xu et al., soil moisture, with overall weak or no (Chapter 11 Supplementary Material
2017; Padrón et al., 2020). 2019a)( Chapter 11 change (Chapter 11 Supplementary (11.SM)) (Cook et al., 2020), surface soil
Supplementary Material Material (11.SM))(Cook et al., 2020), moisture, which suggest drying (Dai et
(11.SM)) and slight decrease in slight drying in surface soil moisture al., 2018; Lu et al., 2019; Cook et al.,
drought severity in SPEI-PM (Xu et al., 2019a)(Chapter 11 2020)(Chapter 11 Supplementary
(Naumann et al., 2018; Gu et Supplementary Material (11.SM)) and Material (11.SM)), and PDSI (Dai et al.,
al., 2020). tendency to wetting trend in SPEI-PM 2018) and SPEI-PM (Cook et al., 2014b;
(Naumann et al., 2018; Gu et al., Vicente-Serrano et al., 2020a), which
2020). show tendency fo wetting trend.
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Inconsistent trends Low confidence: Mixed signal among
evidence. Inconsistent trends Limited evidence evidence. One study shows and limited evidence. Available studies (Prudhomme et al., 2014;
in one study (Dai and Zhao, inconsistent signals (Touma et studies suggest inconsistent trends in Giuntoli et al., 2015; Touma et al., 2015;
2017) al., 2015) low flow (Zhai et al., 2020b) and the Cook et al., 2020). Some evidence
Do Not Cite, Quote or Distribute 11-231 Total pages: 345
Final Government Distribution Chapter11 IPCC AR6 WGI
SRI (Touma et al., 2015), and (medium confidence) for strong
seasonally inconsistent trends in seasonality of trends, with decrease in
runoff, with decrease in summer and summer and increase in winter (Giuntoli
increase in winter (Cook et al., 2020). et al., 2015; Cook et al., 2020).
N. W. MET Low confidence: Mixed Low confidence: Low confidence: Limited and Medium confidence: Decrease in Medium confidence: Decrease in
North signal with conflicting trends Limited evidence inconsistent evidence. Some meteorological drought severity or meteorological drought severity in the
America depending on the region evidence points to decrease in intensity (Sillmann et al., 2013b; Xu majority of models (Sillmann et al.,
(NWN) (Bonsal et al., 2019; Spinoni meteorological drought severity et al., 2019a; Spinoni et al., 2013b; Swain and Hayhoe, 2015; Touma
et al., 2019; Dunn et al., or intensity based on SPI (Xu et 2020)(Chapter 11 Supplementary et al., 2015; Spinoni et al., 2020)(Chapter
2020). al., 2019a) and CDD (Chapter Material (11.SM)). 11 Supplementary Material (11.SM)).
11 Supplementary Material
(11.SM))
AGR Low confidence: No signal Low confidence: Low evidence: Mixed signal in Low confidence: Mixed signal Low confidence: Mixed signal between
ECOL or inconsistent signals in the Limited evidence changes in drought severity. between different models, drought different models and drought metrics,
duration and severity of Inconsistent changes between metrics and studies, including total including total and surface soil moisture,
droughts based on soil models in CMIP6 and CMIP5 and surfacesoil moisture, as well as PDSI and SPEI-PM (Chapter 11
moisture, PDSI and SPEI and total and surface soil SPEI-PM(Chapter 11 Supplementary Supplementary Material (11.SM)) (Cook
conflicting trend depending moisture(Xu et al., Material (11.SM))(Naumann et al., et al., 2014b, 2020; Dai et al., 2018; Lu
of the subregion (Greve et 2019a)(Chapter 11 2018; Xu et al., 2019a; Cook et al., et al., 2019; Vicente-Serrano et al.,
al., 2014; Dai and Zhao, Supplementary Material 2020; Gu et al., 2020). 2020a), with slight larger tendency
2017; Park Williams et al., (11.SM)); SPEI-PM also towards wetting.
2017; Spinoni et al., 2019; suggests inconsistent changes
Padrón et al., 2020). drought severity (Naumann et
al., 2018; Gu et al., 2020).
HYDR Low confidence: Limited Low confidence: Low confidence: Limited Low confidence: Limited evidence Low confidence: Mixed signal among
evidence. Regionally Limited evidence evidence. One study shows and inconsistent signals in available studies (Prudhomme et al., 2014;
inconsistent trends in one lack of signal (Touma et al., studies (Touma et al., 2015; Cook et Giuntoli et al., 2015; Touma et al., 2015;
study (Dai and Zhao, 2017) 2015) al., 2020; Zhai et al., 2020b) Cook et al., 2020), but slight stronger
tendency towards wetting.
1
2 [END TABLE 11.21 HERE]
3
4
5
6
7
8
9
10
11
12
13
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