CMIP6模式对青藏高原极端气温指数模拟能力评估及预估

利用第六次国际耦合模式比较计划（CMIP6）28个全球气候模式模拟的历史和多SSP排放情景下的模拟结果以及国家气候中心制作的CN05.1格点化的观测数据,在评估28个全球气候模式对青藏高原极端气温相关指数模拟效果的基础上,预估了多个SSP情景下青藏高原未来极端气温指数的变化趋势。评估结果表明多模式集合平均模拟结果更稳定,且能模拟出极端气温指数的时间分布以及空间分布特征,但与观测相比,不同指数存在不同偏差。预估结果表明,相对于1995-2014年,青藏高原上日最高气温最高值（TXx）、日最低气温最低值（TNn）、暖昼指数（TX90p）未来呈上升趋势,霜冻日数（FD）、冰冻日数（ID）、冷夜指数（TN10p）呈减少趋势,其中高原极端低温比极端高温增温明显,暖昼指数在高原西南部增加明显,霜冻日数、冰冻日数、冷夜指数在高原东南部减少明显。SSP1-1.9情景下,极端气温指数在21世纪的变化幅度较小,随着辐射强迫增大,指数的变化趋势也增大。SSP1-2.6情景下,2030年前中国实现碳达峰时,青藏高原地区TXx、 TNn、 TX90p增长分别不超过1.12℃、0.84℃、 8.4%, FD、 I...     

BCC气候模式对中国近50a极端气候事件的模拟评估

利用中国区域437站1958—2005年逐日气温和降水资料,评估了国家气候中心（BeijingCli-mateCenter,BCC）气候模式对中国近50a极端气候事件空间分布、时间演变等方面的模拟能力。结果表明：1）模式对极端温度和降水多年平均的空间分布具有一定的模拟能力,但尚存在系统性的偏差。暖昼日数的模拟优于冷夜日数,全年冷夜日数的模拟优于冬季和夏季的模拟,而全年和夏季暖昼日数的模拟优于冬季的模拟。极端降水频次的模拟优于极端降水量的模拟;夏季和全年的模拟优于冬季的模拟。夏季极端降水频次的模拟较好,但冬季的模拟在长江中下游和华南偏小、北方偏大,而全年的模拟在长江下游及南部沿海地区系统性偏大;夏季和全年极端降水量的模拟系统性偏低,而冬季在北方偏高、南方偏低。2）模式较好地模拟出了夏季和全年冷夜日数的全国较为一致的减少趋势,再现东北和东南沿海地区冬季冷夜日数的减少趋势,但模拟的趋势较实测偏弱。模式对暖昼日数长期趋势的模拟效果较理想,较好地反映出了大部分地区暖昼事件发生频率显著增加的特征,但冬季的模拟尚有待改进。模式较好地模拟出了夏季和全年极端降水频次的长期趋势,较好地刻画了极端降水频次＂南增北减＂的特征;模式对冬季极端降水频次的变化趋势几乎无模拟能力。同样,模式也较好地模拟出了极端降水量夏季南增北减的分布形势和冬季的总体增加趋势,但对全年的模拟不理想。3）模式能较好地模拟出冷夜日数和暖昼日数异常变化的主要空间型,对EOF第一模态的时间演变特征具有一定模拟能力;但模式对第二模态时间演变特征的刻画能力较差。模式对极端降水指标的年际变化具有一定的模拟能力,对部分区域极端降水事件的年际变化具有较好的模拟能力;但模拟能力表现出了明显的区域性差异,部分区域极端降水年际变化的模拟结果与实况甚至相反,模式对极端降水年际变化的模拟能力还有待提高。所得结果可为BCC气候模式的改进及极端气候模拟、预估提供一定的参考。     

全球海气耦合模式对我国极端强降水模拟检验

以1961—1999年我国地面观测逐日降水资料作为观测基础, 初步分析了18个全球海气耦合模式对我国20世纪极端强降水的模拟能力。分析模式对不同级别降水的模拟发现, 各模式模拟的我国1~10 mm小雨日数普遍明显偏多; 10~25 mm中雨日数的模拟结果总体上也以偏多为主, 虽然部分模式能够模拟出我国南方存在的高值中心, 但位置偏北至长江中下游地区; 25~50 mm大雨日数在我国南方明显偏少, 并且大值中心的位置基本都没能模拟出来; 50 mm以上暴雨日数的模拟结果也明显偏小, 除MIROC3.2(hires) 外大部分模式在长江以南地区的结果都未超过2 d; 大部分模式不能正确模拟出我国东部地区大雨日数变化趋势的空间分布。进一步分析各模式对极端强降水的模拟发现:各模式极端强降水阈值明显低于观测; 半数左右的模式模拟出了1961—1999年西北西部极端降水增加的趋势, 个别模式趋势系数的大小与观测相当, 大部分模式对东北和长江中下游地区的模拟结果呈与观测反向的变化趋势, 没有模式能够模拟出我国东部地区存在的东北—华北与华中—长江中下游—华南存在的极端强降水日数增加-减少-增加-减少的空间分布; 大部分模式模拟的极端强降水日数标准差与观测结果比较接近, 这可能主要是由于对观测和各模式使用了同样的判定极端强降水发生的方法。总的来看, 全球海气耦合模式对我国极端强降水的模拟能力还有待进一步改进。     

针对江苏省气温和降水的高分辨率降尺度模拟及预估北大核心

本文利用WRF模式,以25 km分辨率数值模拟结果作为驱动场,对江苏省现代和未来15 a的气候进行5 km高分辨率降尺度模拟及预估。结果显示,高分辨率降尺度模拟对其驱动场具有显著的提高,降水的负偏差和气温冷偏差均有所降低,其模拟的降水与气温概率分布与观测更为接近;对于极端指数,WRF模式能够模拟出其基本分布,除连续湿润日数CWD和极端高温TXx之外,高分辨率模拟对其他指数的模拟均有显著的提升。在RCP8.5排放情景下对未来气候变化的预估表明,江苏降水在夏季以减少为主,在春季则以增加为主,全年平均降水存在减少趋势;未来0~1 mm·d^(-1)的微弱降水发生概率将增加,小雨、中到大雨以及暴雨发生的概率均降低,而暴雨强度的增强导致极端强降水R95显著增加;气温25℃以上高温发生的概率在未来有所增加,而0℃以下的低温发生概率减小,从而导致暖持续日数显著增加,而冷持续日数减小,另外,极端高温和极端低温都有显著的升高。     

1955—2016年甘肃定西地区极端气温变化特征

利用甘肃省定西地区岷县、临洮和华家岭3个站点的气温数据,应用线性趋势拟合等方法,基于10个气温指数,研究了1955—2016年定西地区极端气温变化趋势和特征。结果表明,霜冻日数、结冰日数、冷日日数和冷夜日数4个指数呈下降趋势,而极端最高气温、极端最低气温、夏日日数、热夜日数、冷日日数和冷夜日数6个指数均呈上升趋势。气温变化的第一主周期为30 a,第二主周期为17 a。夏日日数、热夜日数、暖日日数和暖夜日数的变化在气温总体变化中起主要作用。极端最低气温与霜冻日数、冷日日数、冷夜日数显著相关,极端最高气温与夏日日数、暖日日数、暖夜日数显著相关,暖夜日数与霜冻日数也具有较高的相关性。气温指数突变大多发生在1993—1999年。     

青藏高原气温和降水近期、中期与长期变化的预估及其不确定性来源

采用第五次耦合模式比较计划（Coupled Model Intercomparison Project Phase 5,CMIP5）高分辨率全球统计降尺度预估数据集,针对近期（2020—2039年）、中期（2040—2059年）和长期（2080—2099年）,以及全球1.5℃和2℃温升阈值,预估了青藏高原地区平均气温和降水、极端气温和极端降水的变化,定量估算了预估结果的不确定性来源。结果表明：（1）在RCP4.5和RCP8.5情景下,21世纪青藏高原地区平均气温和降水、极端气温和极端降水强度均显著增加,最长连续干旱天气减少。高原气候变化幅度超全球平均,至21世纪末,模式集合预估的气候变化幅度介于全球平均的1.5~3倍。（2）青藏高原地区受0.5℃额外增温的显著影响,年均气温、极端高温和极端低温均显著升高,平均及极端强降水均显著增加。（3）排放情景的选择对近期气候预估影响小,但对长期影响大。在相同排放情景下,内部变率主导了近期高原平均气温预估的不确定性,但至长期其贡献降至10%以下。模式和内部变率的不确定性对降水预估均有贡献,且都随时间减小,最大不确定性中心位于西部和北部边缘,噪声与信号比大于6。     

1961—2005年江苏省降水变化趋势

以江苏省62个气象站1961-2005年降水资料为基础,系统分析了近45年来江苏省降水变化趋势.结果表明:1961-2005年江苏省年降水量无显著变化趋势,秋季降水量呈显著下降趋势,冬季降水量呈显著上升趋势.全省年降水日数呈略有下降趋势,以小于10mm的降水日数下降尤为明显.夏季极端强降水总量、极端强降水事件频率为增加趋势,秋季极端强降水事件强度为下降趋势.     

不同升温情景下中国东北地区平均气候和极端气候事件变化预估

利用区域气候模式RegCM4的逐日气温和降水资料,预估1.5℃和2.0℃升温情景下,东北地区平均气候和极端气候事件的变化。结果表明：RCP4.5排放情景下,模式预计在2030年和2044年左右稳定达到1.5℃和2.0℃升温;两种升温情景下,东北地区气温、积温、生长季长度均呈增加趋势,且增幅随着升温阈值的升高而增加;1.5℃升温情景下,年平均气温增幅为1.19℃,年平均降水距平百分率增幅为5.78%,积温增加247.1℃·d,生长季长度延长7.0 d;2.0℃升温情景下气温、积温、生长季长度增幅较1.5℃升温情景下显著,但是年和四季降水普遍减少,年降水距平百分率减小1.96%。两种升温情景下,极端高温事件显著增加,极端低温事件显著减少,极端降水事件普遍增加。霜冻日数、结冰日数均呈显著减少趋势,热浪持续指数呈显著增加趋势;未来东北地区降水极端性增强,不仅单次降水过程的量级增大,极端降水过程的量级也明显增大,随着升温阈值的增大,极端降水的强度也逐渐增大。     

6.25 km高分辨率降尺度数据对雄安新区及整个京津冀地区未来极端气候事件的预估

基于RCP4.5情景下6.25 km高分辨率统计降尺度数据,使用国际上通用的极端气候事件指数,分析雄安新区及整个京津冀地区未来极端气候事件的可能变化。首先对当代模拟结果进行评估,结果表明,集合平均模拟可以较好地再现大部分极端气候事件指数的分布,且对与气温有关的极端气候事件指数模拟效果较好。但也存在一定偏差,特别是对连续干旱日数(CDD)的模拟效果相对较差。集合平均的预估结果表明,未来在全球变暖背景下,雄安新区及整个京津冀地区均表现为极端暖事件增多,极端冷事件减少,连续干旱日数减少,极端强降水事件增多。具体来看,到21世纪末期,日最高气温最高值(TXx)和日最低气温最低值(TNn)在整个区域上都是增加的,大部分地区增加值分别超过2.4℃和3.2℃;夏季日数(SU)和热带夜数(TR)也都表现为增加,但两者的变化分布基本相反,其中SU在山区增加幅度较大,平原地区增加幅度较小,而TR在平原地区的增加值较山区更显著,两个指数未来增加值分别为20~40 d和5~40 d;霜冻日数(FD)和冰冻日数(ID)都表现为减少,减少值分别超过10 d和5 d;与降水有关的极端气候事件指数,CDD、降雨日数(R_1mm)和中雨日数(R_10mm)的变化均以减少为主,但数值较小,一般都在?10%~0之间;最大5 d降水量(R_X5day)、降水强度(SDII)和大雨日数(R_20mm)主要表现为增加,增加值一般在0~25%之间。从区域平均的变化来看,与气温有关的极端气候事件指数的变化趋势较为显著,与降水有关的极端气候事件指数变化趋势较小。两个区域对比来看,雄安新区模式间的不确定性更大,反映出模式对较小区域模拟的不足。     

1951—2009年天津市主要极端气候指数变化趋势

利用1951—2009年天津市逐日气温和降水量资料,计算了极端气候指数,并研讨了其中11个主要指数的变化特征。结果表明：天津市暖日、冷夜具有基本相反的变化趋势,暖日显著增加,冷夜显著减少,且冷夜的变化幅度远大于暖日。持续暖期、霜日呈显著增加趋势,持续冷期呈显著减少趋势。降水指数中连续干日呈增加趋势,最大连续5 d降水量、日降水强度、强降水率和极强降水率及连续湿日均呈减少趋势,表明天津市干旱化倾向明显。与气温有关的极端指数大多在1993年前后出现突变,周期特征不明显。与降水有关的极端指数未现突变,但周期特征明显,大多出现10 a和4 a的周期。     

Future change of extreme temperature climate indices over East Asia with uncertainties estimation in the CMIP5

How well the climate models simulate extreme temperature over East Asia and how the extreme indices would change under anthropogenic global warming are investigated. The indices studied include hot days (HD), tropical nights (TN), growing degree days (GDD), and cooling degree days (CDD) in summer and heating degree days (HDD) and frost days (FD) in winter. The representative concentration pathway 4.5 (RCP 4.5) experiments for the period of 2075–2099 are compared with historical simulations for the period of 1979–2005 from 15 coupled models that are participated in phase 5 of the Coupled Model Intercomparison Project (CMIP5). To optimally estimate future change and its uncertainty, groups of best models are selected based on Taylor diagrams, relative entropy, and probability density function (PDF) methods previously suggested. Overall, the best models’ multi-model ensemble based on Taylor diagrams has the lowest errors in reproducing temperature extremes in the present climate among three methods. Selected best models in three methods tend to project considerably different changes in the extreme indices from each other, indicating that the selection of reliable models are of critical importance to reduce uncertainties. Three groups of best models show significant increase of summerbased indices but decrease of the winter-based indices. Over East Asia, the most significant increase is seen in the HD (336 ± 23.4% of current climate) and the most significant decrease is appeared in the HDD (82 ± 4.2%). It is suggested that the larger future change in the HD is found over in the Southeastern China region, probably due to a higher local maximum temperature in the present climate. All of the indices show the largest uncertainty over Southeastern China, particularly in the TN (~3.9 times as large as uncertainty over East Asia) and in the HD (~2.4). It is further noted that the TN reveals the largest uncertainty over three East Asian countries (~1.7 and 1.4 over Korea and Japan, respectively). These future changes in extreme temperature events have an important implication for energy-saving applications and human molarity in the future.     

Does CMIP6 Inspire More Confidence in Simulating Climate Extremes over China?

Based on climate extreme indices calculated from a high-resolution daily observational dataset in China during1961–2005, the performance of 12 climate models from phase 6 of the Coupled Model Intercomparison Project(CMIP6),and 30 models from phase 5 of CMIP(CMIP5), are assessed in terms of spatial distribution and interannual variability. The CMIP6 multi-model ensemble mean(CMIP6-MME) can simulate well the spatial pattern of annual mean temperature,maximum daily maximum temperature, and minimum daily minimum temperature. However, CMIP6-MME has difficulties in reproducing cold nights and warm days, and has large cold biases over the Tibetan Plateau. Its performance in simulating extreme precipitation indices is generally lower than in simulating temperature indices. Compared to CMIP5, CMIP6 models show improvements in the simulation of climate indices over China. This is particularly true for precipitation indices for both the climatological pattern and the interannual variation, except for the consecutive dry days. The arealmean bias for total precipitation has been reduced from 127%(CMIP5-MME) to 79%(CMIP6-MME). The most striking feature is that the dry biases in southern China, very persistent and general in CMIP5-MME, are largely reduced in CMIP6-MME. Stronger ascent together with more abundant moisture can explain this reduction in dry biases. Wet biases for total precipitation, heavy precipitation, and precipitation intensity in the eastern Tibetan Plateau are still present in CMIP6-MME, but smaller, compared to CMIP5-MME.     

A Review of Climate Change Attribution Studies

This paper reviews recent progress in climate change attribution studies. The focus is on the attribution of observed long-term changes in surface temperature, precipitation, circulation, and extremes, as well as that of specific extreme weather and climate events. Based on new methods and better models and observations, the latest studies further verify the conclusions on climate change attribution in the IPCC AR5, and enrich the evidence for anthropogenic influences on weather and climate variables and extremes. The uncertainty of global temperature change attributable to anthropogenic forcings lies in the considerable uncertainty of estimated total radiative forcing due to aerosols, while the uncertainty of precipitation change attribution arises from the limitations of observation and model simulations along with influences from large internal variability. In terms of extreme weather and climate events, it is clear that attribution studies have provided important new insights into the changes in the intensity or frequency of some of these events caused by anthropogenic climate change. The framing of the research question, the methods selected, and the model and statistical methods used all have influences on the results and conclusions drawn in an event attribution study. Overall, attribution studies in China remain inadequate because of limited research focus and the complexity of the monsoon climate in East Asia. Attribution research in China has focused mainly on changes or events related to temperature, such as the attribution of changes in mean and extreme temperature and individual heat wave events. Some progress has also been made regarding the pattern of changes in precipitation and individual extreme rainfall events in China. Nonetheless, gaps remain with respect to the attribution of changes in extreme precipitation, circulation, and drought, as well as to the event attribution such as those related to drought and tropical cyclones. It can be expected that, with the continual development of climate models, ongoing improvements to data, and the introduction of new methods in the future, climate change attribution research will develop accordingly. Additionally, further improvement in climate change attribution will facilitate the development of operational attribution systems for extreme events, as well as attribution studies of climate change impacts.     

21世纪前期长江中下游流域极端降水预估及不确定性分析

在全球变暖背景下,极端降水的频率、强度以及持续时间均在显著增加,尤其是对于气候变化敏感的长江中下游流域。由于模式本身、温室气体排放情景以及自然变率存在较大的不确定性,因此未来预估变化的不确定性一直备受关注。为了能够得到对于未来极端降水更为准确的预估结果,使用NEX-GDDP（NASA Earth Exchange Global Daily Downscaled Projections）提供的19个CMIP5降尺度高分辨率数据（0.25°×0.25°）,给出21世纪前期（2016—2035年）长江中下游流域极端降水的可能变化。根据长江中下游流域178个气象站1981—2005年的逐日降水量数据,计算了能够代表极端降水不同特征的指数,在评估模拟能力的基础上给出了21世纪前期RCP4.5情景下极端降水的变化。结果表明,降尺度结果对长江中下游流域极端降水有很好的模拟能力,除R90N外,所有模式模拟其余指数的空间结构与观测的相关系数均超过了0.6。其中所有模式模拟PRCPTOT和R10的相关系数均超过0.95。21世纪前期,长江中下游地区降水趋于极端化,尤其是在流域的西部地区。极端降水日数的变化在减少,表明对于极端降水的贡献主要来自于极端降水日的较大日降水量,而非极端降水日数。未来预估不确定性的大值区主要位于流域的南部地区,流域的西部地区不确定性较低,西部地区极端降水的增加应该受到更多的重视。      

Future Precipitation Extremes in China under Climate Change and Their Physical Quantification Based on a Regional Climate Model and CMIP5 Model Simulations

The atmospheric water holding capacity will increase with temperature according to Clausius-Clapeyron scaling and affects precipitation.The rates of change in future precipitation extremes are quantified with changes in surface air temperature.Precipitation extremes in China are determined for the 21st century in six simulations using a regional climate model,RegCM4,and 17 global climate models that participated in CMIP5.First,we assess the performance of the CMIP5 models and RCM runs in their simulation of extreme precipitation for the current period(RF:1982-2001).The CMIP5 models and RCM results can capture the spatial variations of precipitation extremes,as well as those based on observations:OBS and XPP.Precipitation extremes over four subregions in China are predicted to increase in the mid-future(MF:2039-58)and far-future(FF:2079-98)relative to those for the RF period based on both the CMIP5 ensemble mean and RCM ensemble mean.The secular trends in the extremes of the CMIP5 models are predicted to increase from 2008 to 2058,and the RCM results show higher interannual variability relative to that of the CMIP5 models.Then,we quantify the increasing rates of change in precipitation extremes in the MF and FF periods in the subregions of China with the changes in surface air temperature.Finally,based on the water vapor equation,changes in precipitation extremes in China for the MF and FF periods are found to correlate positively with changes in the atmospheric vertical wind multiplied by changes in surface specific humidity(significant at the p<0.1 level).     

Assessment of indices of temperature extremes simulated by multiple CMIP5 models over China

Given that climate extremes in China might have serious regional and global consequences, an increasing number of studies are examining temperature extremes in China using the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. This paper investigates recent changes in temperature extremes in China using 25 state-of-the-art global climate models participating in CMIP5. Thirteen indices that represent extreme temperature events were chosen and derived by daily maximum and minimum temperatures, including those representing the intensity (absolute indices and threshold indices), duration (duration indices), and frequency (percentile indices) of extreme temperature. The overall performance of each model is summarized by a "portrait" diagram based on relative root-mean-square error, which is the RMSE relative to the median RMSE of all models, revealing the multi-model ensemble simulation to be better than individual model for most indices. Compared with observations, the models are able to capture the main features of the spatial distribution of extreme temperature during 1986-2005. Overall, the CMIP5 models are able to depict the observed indices well, and the spatial structure of the ensemble result is better for threshold indices than frequency indices. The spread amongst the CMIP5 models in different subregions for intensity indices is small and the median CMIP5 is close to observations; however, for the duration and frequency indices there can be wide disagreement regarding the change between models and observations in some regions. The model ensemble also performs well in reproducing the observational trend of temperature extremes. All absolute indices increase over China during 1961-2005.     

CMIP6全球气候模式对中国极端降水模拟能力的评估及其与CMIP5的比较

对CMIP6全球气候模式在中国地区极端降水的模拟能力进行了综合评估。基于CN05.1观测数据集和32个CMIP6全球气候模式的降水数据,采用8个常用极端降水指数对极端降水进行了定量描述。研究结果表明,在极端降水的气候平均态方面,CMIP6多模式集合对1961—2005年中国地区区域平均的8个极端降水指数模拟的平均相对误差为29.94%,相较CMIP5降低了2.95个百分点。极端降水的气候变率方面,CMIP6多模式集合对区域平均的8个极端降水指数模拟的平均相对误差为10.10%,相较CMIP5降低5.45个百分点。此外,利用TS评分进行模式间比较,CMIP6的平均分（0.78）高于CMIP5（0.75）,且模拟能力排名前五的模式中CMIP6占4个。对比14个同源模式的TS评分可以发现,CMIP6（0.91）相对于CMIP5（0.68）的模拟能力显著提高。进一步研究发现,CMIP6相对于CMIP5对不同区域极端降水模拟能力的改进有所区别:CMIP6对干旱区平均的气候态和变率方面改进明显,而对于湿润区的改进主要表现在对极端降水空间相关模拟能力的提高。综上,在中国地区,CMIP6相较于CMIP5对极端降水的模拟能力总体上有提升。      

An intercomparison of model-simulated in extreme rainfall and temperature events during the last half of the twentieth century. Part 1: mean values and variability

In this study we examine the performance of eight of the IPCC AR4 global coupled climate models used in the WCRP CMIP3 Multimodel Dataset, as well as their ensemble mean, in simulating annual indices of extreme temperature and precipitation climate events in South America. In this first part we focus on comparing observed and modeled mean values and interannual variability. Two extreme temperature indices based on minimum temperature (warm nights and frost days) and three indices of extreme precipitation (R95t, R10 and consecutive dry days), obtained both from meteorological stations during 1961–2000 and model outputs, were compared. The number of warm nights are better represented by models than the FD. The interannual variability pattern is also in good agreement with the observed values. For precipitation, the index that is best represented by the models is the R95t, which relates the extreme precipitation to local climate. The maximum of dryness observed over the central Argentinian Andes or the extensive dry season of the Amazon region could not be represented by any model.     

Changes in temperature and precipitation extremes in the CMIP5 ensemble

Twenty-year temperature and precipitation extremes and their projected future changes are evaluated in an ensemble of climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), updating a similar study based on the CMIP3 ensemble. The projected changes are documented for three radiative forcing scenarios. The performance of the CMIP5 models in simulating 20-year temperature and precipitation extremes is comparable to that of the CMIP3 ensemble. The models simulate late 20th century warm extremes reasonably well, compared to estimates from reanalyses. The model discrepancies in simulating cold extremes are generally larger than those for warm extremes. Simulated late 20th century precipitation extremes are plausible in the extratropics but uncertainty in extreme precipitation in the tropics and subtropics remains very large, both in the models and the observationally-constrained datasets. Consistent with CMIP3 results, CMIP5 cold extremes generally warm faster than warm extremes, mainly in regions where snow and sea-ice retreat with global warming. There are tropical and subtropical regions where warming rates of warm extremes exceed those of cold extremes. Relative changes in the intensity of precipitation extremes generally exceed relative changes in annual mean precipitation. The corresponding waiting times for late 20th century extreme precipitation events are reduced almost everywhere, except for a few subtropical regions. The CMIP5 planetary sensitivity in extreme precipitation is about 6 %/°C, with generally lower values over extratropical land.