Comparison of regional characteristics of land precipitation climatology projected by an MRI-AGCM multi-cumulus scheme and multi-SST ensemble with CMIP5 multi-model ensemble projections

Abstract. Climate change is expected to exacerbate the frequency and intensity of drought in many water-limited regions. However, future drought changes in Australia –the driest inhabited continent on Earth– have remained stubbornly uncertain due to a lack of model agreement in projected precipitation changes in most regions. We use an ensemble of future projections from the National Hydrological Projections to assess future drought changes in Australia. The ensemble of 32 simulations was created using the Australian Landscape Water Balance model (AWRA-L) forced by four global climate models (GCMs) from the Coupled Model Intercomparison Project phase 5 that were downscaled and bias-corrected using four alternative methods. This ensemble provided an opportunity to analyse multiple sources of uncertainty on the future projections and quantify changes across multiple drought types (meteorological, hydrological and agricultural). We show future increases for all three drought types, with largest increases projected in winter and spring. The sign of the changes is consistent across different drought metrics but projected changes are more robust for the time spent under drought than drought duration or intensity. The future changes are particularly robust in the highly populated and agricultural regions of Australia, suggesting potential impacts on agricultural activities, ecosystems and urban water supply. We attributed uncertainty in future drought changes to GCMs, downscaling/bias correction (DS-BC) methods and emissions scenarios. GCMs represent the largest source of uncertainty (47 %–72 % of the full range of projections) but the choice of DS-BC method is also important (23 %–58 %, with approximately half of this uncertainty arising from the choice between dynamical and statistical downscaling). The emissions scenarios were the lowest source of uncertainty (11 %–33 %) but influenced the magnitude and spatial extent of robust future changes. Overall, the projections suggest likely future increases in drought in Australia with little evidence for ameliorating drought risk with climate change despite ongoing uncertainty in future changes in parts of the country.

California is one of the major uncertainty hotspots for climate change, as climate models have historically been split between projecting wetter and drier future conditions over the region. We analysed the future (mid‐century and end‐century) projections of California's winter precipitation changes from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6), and studied its respective model agreement in comparison to the previous CMIP5 projections. Over northern California more than two thirds of the models in each ensemble agree on wetter future conditions. However, over southern California both ensembles show highly uncertain precipitation changes, with model projections almost equally divided between wetter or drier conditions. Projected end‐century precipitation changes range from −30% to +70% in CMIP5 and −20% to +80% in CMIP6. The CMIP6 ensemble mean changes are generally wetter and show larger model disagreement compared to CMIP5. Distribution of year‐to‐year precipitation indicates more extremely wet or dry years over southern California in CMIP6 compared to CMIP5, with some models suggesting that the five wettest years account for as much as ~55% of the 20‐year rainfall, and the five driest for as little as ~5%. Dynamically, both ensembles project weakened subsidence over Baja California that is stronger in CMIP6 than in CMIP5, in line with the wetter mean conditions in CMIP6. In the western tropical Pacific we find strengthening of the Hadley circulation in CMIP6 that is not seen in CMIP5, and more El Niño than La Niña conditions in the equatorial Pacific. More CMIP6 models also project an increase in ENSO events compared to CMIP5, and a stronger impact of ENSO on California's precipitation is found in CMIP6 than in CMIP5. These factors also contribute to larger model disagreement and more extremely wet or dry years over southern California in CMIP6.

Cold winters in Eurasia considerably affect transportation, agriculture, energy, and public health. This study utilizes 31 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and 33 CMIP5 models to evaluate the historical surface air temperature, sea level pressure, 500‐hPa geopotential height, 150‐hPa meridional and zonal wind, and polar vortex indices during cold winters. Our research quantifies the advancements of CMIP6 over CMIP5. Additionally, future changes in these variables under three different Shared Socioeconomic Pathways (SSPs), that is, SSP 1–2.6, SSP 2–4.5, and SSP 5–8.5, are projected based on 20 out of the 31 CMIP6 models. The results indicate that the multimodel ensemble means from both CMIP5 and CMIP6 effectively capture the main features of the observed Eurasian cold winters and their associated factors with good simulation agreement. The CMIP6 ensemble mean outperforms its CMIP5 counterpart, and both ensemble means (CMIP5 and CMIP6) perform better than individual CMIP6 models. Among CMIP6 models, 500‐hPa geopotential height achieves the highest simulation skill, whereas sea level pressure shows the lowest. Compared with same‐institute models from CMIP5, CMIP6 models show overall improvements with sea level pressure simulation being notably advanced. Under the three SSPs, the occurrence probability of cold winters is projected to decrease as the area and intensity indices of the polar vortex decline. Moreover, surface temperature anomalies are projected to exhibit a “warm Arctic and cold Eurasia” pattern, and the anticyclonic anomalies at 500 hPa and 150 hPa are projected to be centered at high latitudes.

Uncertainty in the strength of the Atlantic Meridional Overturning Circulation (AMOC) is analyzed in the Coupled Model Intercomparison Project Phase 3 (CMIP3) and Phase 5 (CMIP5) projections for the twenty-first century; and the different sources of uncertainty (scenario, internal and model) are quantified. Although the uncertainty in future projections of the AMOC index at 30°N is larger in CMIP5 than in CMIP3, the signal-to-noise ratio is comparable during the second half of the century and even larger in CMIP5 during the first half. This is due to a stronger AMOC reduction in CMIP5. At lead times longer than a few decades, model uncertainty dominates uncertainty in future projections of AMOC strength in both the CMIP3 and CMIP5 model ensembles. Internal variability significantly contributes only during the first few decades, while scenario uncertainty is relatively small at all lead times. Model uncertainty in future changes in AMOC strength arises mostly from uncertainty in density, as uncertainty arising from wind stress (Ekman transport) is negligible. Finally, the uncertainty in changes in the density originates mostly from the simulation of salinity, rather than temperature. High-latitude freshwater flux and the subpolar gyre projections were also analyzed, because these quantities are thought to play an important role for the future AMOC changes. The freshwater input in high latitudes is projected to increase and the subpolar gyre is projected to weaken. Both the freshening and the gyre weakening likely influence the AMOC by causing anomalous salinity advection into the regions of deep water formation. While the high model uncertainty in both parameters may explain the uncertainty in the AMOC projection, deeper insight into the mechanisms for AMOC is required to reach a more quantitative conclusion.

The effects of future tropical Pacific sea surface temperature (SST) changes on regional precipitation projections are statistically studied for December–January–February (DJF) and June–July–August (JJA) in the Coupled Model Intercomparison Project phase 3 (CMIP3) experiments. The present climate precipitation responses to Niño3 SST variability appear as an uncertainty with regards to future regional precipitation changes among the CMIP3 model projections. Compared with the CMIP3 models projecting La Niña-like Pacific SST changes, the models projecting El Niño-like Pacific SST changes tend to simulate more precipitation in future DJF over the tropical central Pacific, southeastern North America and the tropical western Indian Ocean, and less over the tropical northwestern Pacific, the tropical South Pacific and tropical South America. For JJA, the models projecting El Niño-like Pacific SST changes tend to simulate greater future precipitation in the tropical central Pacific and less over the Maritime Continent and around Central America. Interestingly, the present climate features of the delayed JJA precipitation response to previous DJF Niño3 SST variability also appear as differences in future JJA precipitation changes between the models projecting future El Niño-like and La Niña-like Pacific SST changes in DJF. Compared to the later models, the former models have a tendency to show more precipitation south of Japan and south of the equator in the central to eastern Pacific, and less in the subtropical northwestern Pacific. CMIP3 model analysis indicates that the projected El Niño-like SST changes are related to the present precipitation climatology of the models in the near-equatorial eastern Pacific for each DJF and JJA season, suggesting the importance of realistically simulating present precipitation climatology in the tropical Pacific for future projections.

Assessing the performance of General Circulation Models (GCMs) in simulating historical regional precipitation is essential for climate assessments. This study analysed 18 GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 27 GCMs from Phase 6 (CMIP6) for the Florida Peninsula. Naïve combinations formed the CMIP5‐Ensemble and CMIP6‐Ensemble. The reference dataset consisted of global gridded precipitation data from National Oceanic and Atmospheric Administration. GCM‐simulated precipitation data were compared to the reference dataset across multiple time scales between 1951 and 2005. Most CMIP5 and CMIP6 GCMs exhibited a negative bias at the daily time scale, with an absolute value ranging between 0 and 2 mm day−1 at the median level, except for INM‐CM4 and MRI‐CGCM3 (CMIP5) and CNRM‐ESM2‐1 (CMIP6). Two performance metrics, that is, Pearson correlation and mean absolute error (MAE), were used to evaluate the GCMs. A correlation value above 0.3 is statistically significant at the significance level (α = 0.05). For the Pearson correlation between simulated and observed monthly precipitation, only 15% of CMIP5 GCMs (3 out of 19) had a median value above 0.3, whilst this increased to approximately 39% (11 out of 28) for CMIP6 GCMs. For monthly climatological precipitation, only 21% (4 out of 21) of GCMs in CMIP5 showed a correlation with a median value of 0.8 or higher. This percentage rose to 50% (14 out of 28) for CMIP6. A notable difference was observed in the MAE between CMIP5 and CMIP6 climate models. Lastly, GCM raw outputs were evaluated based on rainy season characteristics (onset, demise and duration). Significant improvements from the CMIP5 to the CMIP6 models were observed in capturing the rainy season in the Florida Peninsula. This study thoroughly compares CMIP5 and CMIP6 climate models in simulating historical precipitation at various time scales for the study region. Although the results are specific to the study area, the methods can be applied more broadly.

This study examines future changes in precipitation and temperature across Myanmar using bias-corrected multimodel ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6) archive under shared socioeconomic pathway 2-4.5 (SSP2-4.5) and SSP5-8.5 scenarios. Thirteen general circulation models (GCMs) were evaluated against observed data from 38 meteorological stations (1985–2014), and seven models were selected based on performance metrics. Bias correction was applied using the power transformation method for precipitation and variance scaling for temperature, and its performance was assessed for reproducing both baseline climate and extreme indices. Decomposition of uncertainty sources for the baseline period revealed that model and temporal variability are the dominant contributors to projection uncertainty. Future projections were analyzed for near-future (2021–50), mid-future (2051–80), and far-future (2081–2100) periods. Results indicate increasing precipitation across most regions, particularly during the rainy season, with the central dry zone and coastal areas showing the most notable changes. Minimum temperatures show a consistent warming trend across all scenarios and regions. In contrast, maximum temperatures exhibit mixed trends under SSP2-4.5 but a pronounced increase under SSP5-8.5, with the northern hilly region projected to warm by up to 4.9°C by the end of the century. Extreme indices also show a clear intensification of extremes toward the end of the century, especially under SSP5-8.5. These findings offer essential insights into Myanmar’s future climate risks and provide a scientific foundation for developing region-specific climate adaptation and resilience strategies. Significance Statement This study applies CMIP6 multimodel ensembles to project long-term precipitation and temperature changes across Myanmar under shared socioeconomic pathway 2-4.5 (SSP2-4.5) and SSP5-8.5. By bias-correcting model outputs and selecting high-performing general circulation models (GCMs) and analyzing both mean climate and extreme indices, we provide robust regional and seasonal climate projections through 2100. The decomposition of uncertainty sources for the base period and projection period shows how model and temporal variability affect the projections. The results reveal significant warming and shifting precipitation patterns, particularly affecting the dry and hilly zones. These changes have critical implications for agriculture, water resources, and climate risk management. Our work addresses a regional knowledge gap in climate projection literature and offers actionable information for adaptation planning in one of Southeast Asia’s most climate-vulnerable countries.

Knowledge about future global and regional warming is essential for effective adaptation planning and our current temperature projections are based on the output of global climate models (GCMs). Although GCMs agree on the direction of change, there are still significant discrepancies in the magnitude of the projected response1.  Here we develop a novel method2,3 for constraining uncertainty in future regional temperature projections based on the predictions of an observationally trained machine learning algorithm, Ridge-ERA5. Ridge-ERA5 - a Ridge regression model4- learns coefficients to represent observed relationships between daily temperature anomalies and a selection of thermodynamic and dynamical variables in the ECMWF Re-Analysis (ERA) 5 dataset5. Climate-invariance of the Ridge relationships is demonstrated in a perfect model framework: we train a set of 23 Ridge-CMIP models on historical data of the Coupled Model Intercomparison Project (CMIP) phase 66 and evaluate their predictions using future scenario data from the most extreme future emissions pathway, SSP 5-8.5.   Combining the historically constrained Ridge-ERA5 coefficients with normalised inputs from CMIP6 future climate change simulations forms the basis of a new methodology to derive observational constraints on regional climate change. For daily, regional (2°x2°), summer temperatures across the Northern Hemisphere, the Ridge-ERA5 observations-based constraint implies, for example, that a group of higher sensitivity CMIP6 models is inconsistent with observational evidence (including in Eastern, West & Central, and Northern Europe) potentially suggesting that the sensitivity of these models is indeed too high7,8. A key advantage of our new method is the ability to constrain regional projections at very high – daily – temporal resolution which includes extreme events such as heatwaves.    1) Brient, F. (2019) Reducing Uncertainties in Climate Projections with Emergent Constraints: Concepts, Examples and Prospects. Advances in Atmospheric Sciences 2020 37:1, 37(1), pp. 1–15.  2) Ceppi, P. and Nowack, P. (2021) Observational evidence that cloud feedback amplifies global warming. PNAS, 118(30).  3) Nowack, P. et al. An observational constraint on the uncertainty in stratospheric water vapour projections. (in review)  4) Hoerl, A. E. and Kennard, R. W. (1970) Ridge Regression: Applications to Nonorthogonal Problems. Technometrics, 12(1), pp. 69–82.   5) Hersbach, H. et al. (2020) The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), pp. 1999–2049.   6) Eyring, V. et al. (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), pp. 1937–1958.   7) Zelinka, M. D. et al. (2020) Causes of Higher Climate Sensitivity in CMIP6 Models. Geophysical Research Letters, 47(1).  8) Zhu, J., Poulsen, C. J. and Otto-Bliesner, B. L. (2020) High climate sensitivity in CMIP6 model not supported by paleoclimate. Nature Climate Change 2020 10:5, 10(5), pp. 378–379. 

ABSTRACTA set of 27 global climate models from the Coupled Model Inter‐comparison Project Phase 5 (CMIP5) ensemble are assessed for their performance for the purpose of making future climate projection studies in the western tropical Pacific and differences to Coupled Model Inter‐comparison Project Phase 3 (CMIP3) are assessed. The CMIP5 models show some improvements upon CMIP3 in the simulation of the climate in the western tropical Pacific in the late 20th century. There are fewer CMIP5 models with very poor skill scores than in CMIP3 for some measures and a small group of the well‐performing models in CMIP5 have lower biases than in an equivalent group from CMIP3. These best‐performing models could be particularly informative for studying certain climate sensitivities and feedbacks in the region. There is evidence to reject one model as unsuitable for making regional climate projections in the region, and another two models unsuitable for analysis of the South Pacific Convergence Zone (SPCZ). However, while there have been improvements, many of the systematic model biases in the mean climate in CMIP3 are also present in the CMIP5 models. They are primarily related to the shape of the transition between the Indo‐Pacific warm pool and equatorial cold tongue, and the associated biases in the position and orientation of the SPCZ and Inter‐Tropical Convergence Zone, as well as in the spatial pattern, variability and teleconnections of the West Pacific monsoon, and the simulation of El Niño Southern Oscillation. Overall, the results show that careful interpretation and consideration of biases is required when using CMIP5 outputs for generating regional climate projections for the western tropical Pacific, particularly at the country scale, just as there was with CMIP3.

Abstract. Poor air quality is currently responsible for large impacts on human health across the world. In addition, the air pollutants ozone (O3) and particulate matter less than 2.5 µm in diameter (PM2.5) are also radiatively active in the atmosphere and can influence Earth's climate. It is important to understand the effect of air quality and climate mitigation measures over the historical period and in different future scenarios to ascertain any impacts from air pollutants on both climate and human health. The Coupled Model Intercomparison Project Phase 6 (CMIP6) presents an opportunity to analyse the change in air pollutants simulated by the current generation of climate and Earth system models that include a representation of chemistry and aerosols (particulate matter). The shared socio-economic pathways (SSPs) used within CMIP6 encompass a wide range of trajectories in precursor emissions and climate change, allowing for an improved analysis of future changes to air pollutants. Firstly, we conduct an evaluation of the available CMIP6 models against surface observations of O3 and PM2.5. CMIP6 models consistently overestimate observed surface O3 concentrations across most regions and in most seasons by up to 16 ppb, with a large diversity in simulated values over Northern Hemisphere continental regions. Conversely, observed surface PM2.5 concentrations are consistently underestimated in CMIP6 models by up to 10 µg m−3, particularly for the Northern Hemisphere winter months, with the largest model diversity near natural emission source regions. The biases in CMIP6 models when compared to observations of O3 and PM2.5 are similar to those found in previous studies. Over the historical period (1850–2014) large increases in both surface O3 and PM2.5 are simulated by the CMIP6 models across all regions, particularly over the mid to late 20th century, when anthropogenic emissions increase markedly. Large regional historical changes are simulated for both pollutants across East and South Asia with an annual mean increase of up to 40 ppb for O3 and 12 µg m−3 for PM2.5. In future scenarios containing strong air quality and climate mitigation measures (ssp126), annual mean concentrations of air pollutants are substantially reduced across all regions by up to 15 ppb for O3 and 12 µg m−3 for PM2.5. However, for scenarios that encompass weak action on mitigating climate and reducing air pollutant emissions (ssp370), annual mean increases in both surface O3 (up 10 ppb) and PM2.5 (up to 8 µg m−3) are simulated across most regions, although, for regions like North America and Europe small reductions in PM2.5 are simulated due to the regional reduction in precursor emissions in this scenario. A comparison of simulated regional changes in both surface O3 and PM2.5 from individual CMIP6 models highlights important regional differences due to the simulated interaction of aerosols, chemistry, climate and natural emission sources within models. The projection of regional air pollutant concentrations from the latest climate and Earth system models used within CMIP6 shows that the particular future trajectory of climate and air quality mitigation measures could have important consequences for regional air quality, human health and near-term climate. Differences between individual models emphasise the importance of understanding how future Earth system feedbacks influence natural emission sources, e.g. response of biogenic emissions under climate change.

This paper evaluates Indo-Pacific warm pool (IPWP) sea surface temperature (SST) warming biases of Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6. The IPWP warming trend in the CMIP5 multi-model ensemble (MME) is closer to observation than in CMIP6 MME, but the IPWP expanding trend is the opposite. There is no qualitative improvement in the simulation of IPWP warming from CMIP5 to CMIP6. In addition, four metrics were used to investigate the performance of Indo-Pacific region warming trends in all models. CMIP6 models perform better than CMIP5 with smaller root mean square error and bias in MME and higher skill scores in MME and top models, which is tightly linked to their better performance in simulating associated physical processes in CMIP6 models. IPWP warming biases are mainly attributed to the combined effects of positive atmospheric process biases and negative ocean dynamics term biases. The positive atmospheric process biases are primarily related to the shortwave radiation and latent heat flux from atmospheric forcing, the latter of which can be attributed to the biases in surface wind fields. Compared with CMIP5 models, the IPWP warming simulated by CMIP6 models is weaker, related to the less robust atmospheric processes and the shallower thermocline anomalies simulated by CMIP6.

A parallel comparison is made of the circulation climatology and the leading oscillation mode of the northern winter stratosphere among six reanalysis products and 24 CMIP5 (Coupled Model Intercomparison Project Phase 5) models. The results reveal that the NCEP/NCAR, NECP/DOE, ERA40, ERA-Interim and JRA25 reanalyses are quite consistent in describing the climatology and annual cycle of the stratospheric circulation. The 20CR reanalysis, however, exhibits a remarkable “cold pole” bias accompanied by a much stronger stratospheric polar jet, similar as in some CMIP5 models. Compared to the 1–2 month seasonal drift in most coupled general circulation models (GCMs), the seasonal cycle of the stratospheric zonal wind in most earth system models (ESMs) agrees very well with reanalysis. Similar to the climatology, the amplitude of Polar Vortex Oscillation (PVO) events also varies among CMIP5 models. The PVO amplitude in most GCMs is relatively weaker than in reanalysis, while that in most of the ESMs is more realistic. In relation to the “cold pole” bias and the weaker oscillation in some CMIP5 GCMs, the frequency of PVO events is significantly underestimated by CMIP5 GCMs; while in most ESMs, it is comparable to that in reanalysis. The PVO events in reanalysis (except in 20CR) mainly occur from mid-winter to early spring (January–March); but in some of the CMIP5 models, a 1–2 month delay exists, especially in most of the CMIP5 GCMs. The long-term trend of the PVO time series does not correspond to long-term changes in the frequency of PVO events in most of the CMIP5 models.

The Southern Hemisphere (SH) eddy-driven jet stream has been shown to move poleward in climate models in response to greenhouse gas forcing, but the magnitude of the shift is uncertain. Here we address the fact that the latest Coupled Model Intercomparison Project phase 6 (CMIP6) models simulate, on average, a smaller jet shift in response to an abrupt quadrupling in CO2 than the predecessor models (Coupled Model Intercomparison Project phase 5 (CMIP5)), despite producing larger global average surface warming. We focus on the response in the first decade when the majority of the long-term jet shift occurs and when the difference between CMIP5 and CMIP6 models emerges. We hypothesise the smaller poleward jet shift is related to the weaker increase in the meridional sea surface temperature (SST) gradient across the southern extratropics in CMIP6 models. We impose the multi-model mean SST patterns alongside a quadrupling in CO2 in an intermediate complexity general circulation model (IGCM4) and show that many of the regional and seasonal differences in lower tropospheric zonal winds between CMIP5 and CMIP6 models are reproduced by prescribing the SST patterns. The main exception is in austral summer when the imposed SST patterns and CO2 increase in IGCM4 produce weaker differences in zonal wind response compared to those simulated by CMIP5/6 models. Further IGCM4 experiments that prescribe only SH extratropical SSTs simulate a weaker jet shift for CMIP6 SSTs than for CMIP5, comparable to the full experiment. The results demonstrate that SH SST patterns are an important source of uncertainty for the shift of the midlatitude circulation in response to CO2 forcing. The study also provides an alternative explanation than was proposed by Curtis et al (2020 Environ. Res. Lett. 15 64011), who suggested model improvements in jet biases could account for the smaller jet shift in CMIP6 models in the extended austral winter season.

Precipitation variability has great economic, social, and environmental impacts across the globe, and in particular in China. This paper evaluates the historical precipitation variability based on 20 general circulation models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive over the 20th century relative to two observational data sets and quantifies CMIP5 improvements over CMIP3. Multimodel ensemble means and individual models are assessed. Three future emission scenarios are used (representative concentration pathways (RCP) 8.5, RCP 4.5, and RCP 2.6), and 21st century CMIP5 estimates are put into context based on the 20th century biases. We find that CMIP5 models can reproduce the spatial pattern of precipitation over China during the 20th century, which represents an improvement over CMIP3. However, the models overestimate the magnitude of seasonal and annual precipitation in most regions of China, especially along the eastern edge of the Tibetan Plateau, and underestimate summer precipitation over southeastern China. For China as a whole, CMIP5's overestimation of annual precipitation is greater than CMIP3, which can be traced back to a greater underestimation of summer precipitation in CMIP3. There is a large spread among individual models, with the greatest uncertainties in simulating summer precipitation. Trends and correlations also suggest a better agreement of CMIP5 with observations than CMIP3. Throughout the 20th century, both the observations and models show an increasing trend in precipitation over parts of northwestern China and a decreasing trend over the Tibetan Plateau. There is poor agreement in precipitation trends over the southeast and northeast regions. In general, multimodel means cannot capture the amplitude of observed multidecadal precipitation variability. In the 21st century, precipitation is generally projected to increase across all of China under all three scenarios. RCP 8.5 exhibits the largest significant trend at a rate of +1.5 mm/yr, corresponding to 16% precipitation increase by the end of the century. The RCP 2.6 scenario shows the smallest increases, at +0.5 mm/yr (6%) by 2100. The greatest increases are projected to occur over the Tibetan Plateau and eastern China in summer, suggesting an altered monsoonal circulation in the future. However, due to the uncertainties in CMIP5, future precipitation projections should be interpreted with caution.

This paper provides a first overview of the performance of state‐of‐the‐art global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating climate extremes indices defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), and compares it to that in the previous model generation (CMIP3). For the first time, the indices based on daily temperature and precipitation are calculated with a consistent methodology across multimodel simulations and four reanalysis data sets (ERA40, ERA‐Interim, NCEP/NCAR, and NCEP‐DOE) and are made available at the ETCCDI indices archive website. Our analyses show that the CMIP5 models are generally able to simulate climate extremes and their trend patterns as represented by the indices in comparison to a gridded observational indices data set (HadEX2). The spread amongst CMIP5 models for several temperature indices is reduced compared to CMIP3 models, despite the larger number of models participating in CMIP5. Some improvements in the CMIP5 ensemble relative to CMIP3 are also found in the representation of the magnitude of precipitation indices. We find substantial discrepancies between the reanalyses, indicating considerable uncertainties regarding their simulation of extremes. The overall performance of individual models is summarized by a “portrait” diagram based on root‐mean‐square errors of model climatologies for each index and model relative to four reanalyses. This metric analysis shows that the median model climatology outperforms individual models for all indices, but the uncertainties related to the underlying reference data sets are reflected in the individual model performance metrics.