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The representation and projection of extreme temperature and precipitation events in regional and global climate models are of major importance for the study of climate change impacts. However, state-of-the-art global and regional climate model simulations yield a broad inter-model range of intensity, duration and frequency of these extremes. Here, we present a modeling experiment using the Weather Research and Forecasting (WRF) model to determine the influence of the land surface model (LSM) component on uncertainties associated with extreme events. First, we analyze land–atmosphere interactions within four simulations performed by the WRF model from 1980 to 2012 over North America, using three different LSMs. Results show LSM-dependent differences at regional scales in the frequency of occurrence of events when surface conditions are altered by atmospheric forcing or land processes. The inter-model range of extreme statistics across the WRF simulations is large, particularly for indices related to the intensity and duration of temperature and precipitation extremes. Our results show that the WRF simulation of the climatology of heat extremes can be 5 ∘ C warmer and 6 d longer depending on the employed LSM component, and similarly for cold extremes and heavy precipitation events. Areas showing large uncertainty in WRF-simulated extreme events are also identified in a model ensemble from three different regional climate model (RCM) simulations participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX) project, revealing the implications of these results for other model ensembles. Thus, studies based on multi-model ensembles and reanalyses should include a variety of LSM configurations to account for the uncertainty arising from this model component or to test the performance of the selected LSM component before running the whole simulation. This study illustrates the importance of the LSM choice in climate simulations, supporting the development of new modeling studies using different LSM components to understand inter-model differences in simulating extreme temperature and precipitation events, which in turn will help to reduce uncertainties in climate model projections. [ABSTRACT FROM AUTHOR]

When analyzing multimodel climate ensembles it is often assumed that the ensemble is either truth centered or that models and observations are drawn from the same distribution. Here we analyze CMIP5 ensembles focusing on three measures that separate the two interpretations: the error of the ensemble mean relative to the error of individual models, the decay of the ensemble mean error for increasing ensemble size, and the correlations of the model errors. The measures are analyzed using a simple statistical model that includes the two interpretations as different limits and for which analytical results for the three measures can be obtained in high dimensions. We find that the simple statistical model describes the behavior of the three measures in the CMIP5 ensembles remarkably well. Except for the large-scale means we find that the indistinguishable interpretation is a better assumption than the truth centered interpretation. Furthermore, the indistinguishable interpretation becomes an increasingly better assumption when the errors are based on smaller temporal and spatial scales. Building on this, we present a simple conceptual mechanism for the indistinguishable interpretation based on the assumption that the climate models are calibrated on large-scale features such as, e.g., annual means or global averages. [ABSTRACT FROM AUTHOR]

Ecotron facilities allow accurate control of many environmental variables coupled with extensive monitoring of ecosystem processes. They therefore require multivariate perturbation of climate variables, close to what is observed in the field and projections for the future. Here, we present a new method for creating realistic climate forcing for manipulation experiments and apply it to the UHasselt Ecotron experiment. The new methodology uses data derived from the best available regional climate model projection and consists of generating climate forcing along a gradient representative of increasingly high global mean air temperature anomalies. We first identified the best-performing regional climate model simulation for the ecotron site from the Coordinated Regional Downscaling Experiment in the European domain (EURO-CORDEX) ensemble based on two criteria: (i) highest skill compared to observations from a nearby weather station and (ii) representativeness of the multi-model mean in future projections. The time window is subsequently selected from the model projection for each ecotron unit based on the global mean air temperature of the driving global climate model. The ecotron units are forced with 3-hourly output from the projections of the 5-year period in which the global mean air temperature crosses the predefined values. With the new approach, Ecotron facilities become able to assess ecosystem responses on changing climatic conditions, while accounting for the co-variation between climatic variables and their projection in variability, well representing possible compound events. The presented methodology can also be applied to other manipulation experiments, aiming at investigating ecosystem responses to realistic future climate change. [ABSTRACT FROM AUTHOR]

The diurnal temperature range (DTR) is an identifiable and sensitive indicator of the synchronicity of changes in diurnal temperature extrema, but capturing DTR dynamics is challenging for climate models. This study investigates the climatology, variability, and changes of DTR in recent models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). The results show that the CMIP6 models underestimate DTR climatology relative to observations. Most individual models overestimate December-February variability, particularly at high latitudes of the Northern Hemisphere. The models show substantially different changes over land surfaces and do not fully capture the observed spatiotemporal evolution of DTR. Large intermodel differences seem to be controlled by daily minimum air temperature. In the Northern Hemisphere, precipitation and cloud longwave and shortwave radiative effects appear tomake important contributions to the intermodel discrepancies. Evaporative fraction is an important factor contributing to the intermodel differences in DTR during the summer in the Northern Hemisphere. In general, CMIP6 models have not improved their ability to simulate temporal DTR changes in a consistent way over the entire analysis period (1901-2005) relative to theirCMIP5 counterparts. For periods of rapidDTRdecline (e.g., 1951-80)CMIP6models are typically better than the CMIP5 versions at simulating DTR, whereas for other periods CMIP6 models underperform their CMIP5 counterparts. [ABSTRACT FROM AUTHOR]

The precipitation behaviour at different timescales (interannual, intraseasonal and synoptic) and extreme events in a warmer scenario over South America is analysed. This study is based on a set of Regional Climate Models (RCM) from the CORDEX database and their driving Global Climate Models (GCM) from the CMIP5 Project. Historical simulations for 1979–2005 and from the RCP4.5 scenario for 2071–2100 are used. The projected changes in the precipitation behaviour are evaluated in terms of the consistency between the pairs RCM-GCM aiming to explore the added value of RCMs. The agreement between projected changes from RCMs is also evaluated as a measure of confidence of the regional climate change signal. The analysis is carried out for two extended seasons (April to September and October to March). The projected change in the mean precipitation over subtropical latitudes is associated with both changes in the frequency of rainy days and in the intensity of extreme events, while for the tropics the changes are mainly associated with changes in the wet day frequency. The increase of extreme precipitation events over the subtropics arises as a robust signal among models, while for tropical latitudes the dispersion among models is high, which reduces the confidence of the projections. In general, the consistency of the projections in the pairs RCM-GCM and the agreement among models are higher for the low frequency variability patterns during winter, while for summertime a better agreement in the projected changes of the precipitation behaviour at different timescales among RCMs is found. [ABSTRACT FROM AUTHOR]

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Simulations from the models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6), which represent the most recent generation of climate models, are now available. Understanding the performance of these models in simulating historical climate extremes can provide a basis for producing reliable future climate projections. Here, we assess the simulation of 16 indices of temperature extremes defined by the Expert Team on Climate Change Detection and Indices using results from 24 CMIP6 models as compared with results from CMIP5. Comparisons with observations and reanalyses indicate that the CMIP6 models could capture the spatial patterns and temporal variations of the observed temperature extremes well for some indices, although less well for others. Based on spatial and temporal skill scores, CMIP6 ensemble means were more skillful in simulating absolute and threshold indices of extreme temperature than CMIP5 ensemble means were, but the performances of both the CMIP5 and CMIP6 ensemble means in simulating the spatial patterns for duration and percentile indices were relatively unsatisfactory (spatial skill scores S < 0.3). Furthermore, our results suggest that there have been improvements in spatial pattern skill scores in some individual CMIP6 models relative to CMIP5 model scores for summer days, tropical nights, cold spell duration, and diurnal temperature range. Key Points: The performance of CMIP6 models has been evaluated for simulating global temperature extremesCMIP6 models can capture the spatial patterns and temporal variations of temperature extremes indicesCMIP6 shows improvement over CMIP5 in simulating several absolute and threshold indices of extreme temperature [ABSTRACT FROM AUTHOR]

Landslide hazards in British Columbia are mainly caused by precipitation and can result in significant damage and fatalities. Anthropogenic climate change is expected to increase precipitation frequency and intensity in the winter, spring, and fall in British Columbia (BC), potentially resulting in increased frequency of landslide hazard. Quantifying the effect of changing precipitation on future landslide hazard across the varying topographic and climatic conditions in BC requires detailed projections of future precipitation. Here, the operational Landslide Hazard Assessment for Situational Awareness (LHASA) model is used with high-resolution, statistically downscaled daily precipitation to generate detailed simulations of landslide hazard in BC over the twenty-first century. Historical evaluation of the LHASA model is performed using a station-based, gridded observational precipitation dataset. Classification of observed landslide dates and locations as hazard events occurs as successfully as, or slightly better than, when LHASA is applied globally with satellite precipitation. Using the LHASA model with precipitation projections from 12 downscaled global climate models following RCP8.5 indicates that future landslide hazard frequency will increase from 16 days per year to 21 days per year (32%) on average by the 2050s for landslide susceptible regions in the province. Areas of the province currently with the most frequent landslide hazards (18 to 21 days per year), including the west coast and northern Rocky Mountains, are expected to see between 8 and 11 additional hazardous days (49 to 61% increases) per year. Most of the increased hazard frequency occurs during winter and fall, reflecting those seasons with the largest projected increases in single and multi-day precipitation. Risk assessments for regions in British Columbia vulnerable to landslides will need to account for increasing hazard due to climate change altered precipitation. [ABSTRACT FROM AUTHOR]

In this study, nine global climate models (GCMs) and corresponding downscaled runs by means of the regional climate model (RCM) RCA4 are used to investigate added value (AV) in precipitation and its some drivers over Central Africa (CA). By employing a process‐based analysis approach, we intercompare abilities of RCM to those of driving GCMs in representing the total atmospheric moisture flux convergence (TMFC), moisture transport, and African Easterly Jets (AEJs). Results indicate that simulations with highest AVs in the precipitation climatology also show improvements in the representation of the TMFC and AEJs. Degraded precipitation due to the downscaling is associated with deterioration of at least two of three analyzed mechanisms, and sometimes there is inconsistent AVs between precipitation and related drivers. This sustains that a realistic representation of the moisture transport and atmospheric circulation is of great importance for the correct simulation of present (and, consequently, future) precipitation over CA. Plain Language Summary: Although the downscaling is proved beneficial for the African Coordinated Regional‐climate Downscaling Experiment domain, meaningful climate uncertainties persist; the origins of many biases/deficiencies in RCM results often remain unexplained, in a context where there is a requirement to ensure the fitness for purpose of the models used to generate climate information for decision‐makers. The added value is a statistical metric frequently used to judge whether an RCM has improved the results of the driving GCM or no. Most of the previous studies which have investigated added value over Africa have used the performance‐based evaluation method and are focused only on the diagnostic variables precipitation and temperature and therefore did not investigate reasons behind the RCM's amelioration or degradation. By the means of a process‐based analysis, this work shows that added value from the downscaling must be interpreted with a certain caution over Central Africa, as improvements in the representation of precipitation climatology are not always associated with improvements in related processes. Key Points: Experiments which feature clear improvement in precipitation also show improved moisture fluxes and AEJsDownscaled GCMs showing deteriorated precipitation also show deterioration in at least two of the analyzed processesThere is doubtful added value in some runs, highlighted through the process‐based analysis [ABSTRACT FROM AUTHOR]

At regional to local scales internal variability is expected to be a dominant source of uncertainty in analyzing precipitation extremes and mean precipitation even far into the 21st century. A debated topic is whether a faster increase in subdaily precipitation extremes can be expected. Here we analyzed seasonal maximum precipitation in various time steps (3 hr, days, and 5 days) from a high‐resolution 50‐member large‐ensemble (CRCM5‐LE) and compared them to changes in mean precipitation over Europe. Our results show that the magnitude of change in extreme precipitation varies for season and duration. Subdaily extremes increase at higher rates than daily extremes and show higher scaling with temperature. Northern Europe shows widespread scaling above Clausius‐Clapeyron of subdaily extremes in all seasons and for daily extremes in winter/spring. Scaling above Clausius‐Clapeyron is also visible over Eastern Europe in winter/spring. For most regions and seasons the forced response emerges from the internal variability by midcentury. Plain Language Summary: The knowledge on how and why the intensity and frequency of extremes changes is critical to a resilient society. Our adaptive measures that are currently in place are based on observed extremes of the past. We know that observations are only one realization of a chaotic system and that the climate system is altered by natural variations, and anthropogenic contributions. Since we can only measure one realization of the world, we need climate models to investigate the influence of natural variability and anthropogenic factors. In this study we focus on the contribution of natural variability and the detection of regional patterns of changes in extreme precipitation. We used regional climate simulations, driven by multiple runs of global climate simulations under the same emission scenario, but with slight changes at the start of the simulation to imitate the butterfly effect of the climate system and simulate natural variability. We have found that natural variability plays a dominant role in the first half of the 21st century. But we have also found that subdaily extreme precipitation is increasing at a higher rate than daily extremes and that some of this change can be attributed to the warming of the atmosphere. Key Points: Subdaily extreme precipitation intensifies faster than daily extremes and mean precipitationOn regional scales internal variability remains a dominant source of uncertainty until the end of the 21st centuryWidespread scaling rates of subdaily extremes above Clausius‐Clapeyron over Northern and Eastern Europe [ABSTRACT FROM AUTHOR]

This study aims to project and characterize the climate extremes in Ravi River Basin (RRB) which is considered as a data poor transboundary basin located in India and Pakistan. Performance of the three GCDs against observation data was evaluated at three stations. A quantile mapping technique was used to correct the biases of four regional climate models (RCMs) and climate extremes were analysed for future period (2020–2095). Seven temperature and rainfall based indices that represent warm and wet characteristics of climate were chosen. Four statistical parameters and spatial maps were evaluated for the base period 1982–2005. The CPC‐NOAA and PU had the best performance for temperature and rainfall data regarding the time series analysis. The quantile mapping improved three important aspects of the climate cycle; the transitions from dry to wet and wet to dry seasons and peaks as well. At spatial scale, quantile mapping well captured the spatial distribution of the eleven indices other than RX1Day, CWD and FD0. The results show that warm and wet extremes will increase in future at 5% significance level across the entire basin with large changes in north east. The changes will be large for RCP8.5 scenario compare to RCP4.5 scenario and choice of the scenarios has dominant contribution in uncertainty than choice of the models. [ABSTRACT FROM AUTHOR]

Most of the nature‐related economic costs and human losses in many regions of Europe are due to extreme weather events such as heat waves, cold spells, persistent droughts, heavy precipitation and intense cyclonic wind‐storms. Extreme precipitation events are projected by climatic models to become more intense over the continent while droughts might last longer by the end of the century. In dry regions as Southern Europe, soils are predicted to dry out as temperatures and evapotranspiration rise and rain‐bearing atmospheric circulations become less frequent. Prospects on the future of climate indices linked to extreme phenomena are herein derived by using observed and model projected daily meteorological data. Specifically, E‐OBS high resolution gridded datasets of observed precipitation and surface minimum and maximum temperatures have been used as the regional observed baseline. For projections, the same meteorological variables have been obtained from a set of regional climate models integrated in the European EURO‐CORDEX project, considering the RCP8.5 future emissions scenario. A quantile–quantile adjustment has been applied to the simulated regional scenarios to reduce biases in modelled extreme regimes. Results suggest that warm days will substantially increase across Europe, consistently with a decrease of cold nights. An increase in heat wave amplitude is expected across the continent, with South Eastern Europe and the Mediterranean as the most affected regions. In contrast, Northern Europe will undergo the largest decrease in cold spell magnitude. An overall rise in the frequency and volume of heavy precipitations is projected in all seasons, even if the number of dry days is also expected to increase, except in the Baltic countries. Regarding abnormally long dry periods (extreme droughts), we find that the occurrence of episodes would reduce over Europe as consequence of projected increases in length. [ABSTRACT FROM AUTHOR]

The aim of this study is to investigate extreme precipitation events caused by atmospheric rivers and compare their flood impact in a warmer climate to current climate using an event-based storyline approach. The study was set up by selecting four high-precipitation events from 30 years of present and future climate simulations of the high-resolution global climate model EC-Earth. The two most extreme precipitation events within the selection area for the present and future climate were identified, and EC-Earth was rerun creating 10 perturbed realizations for each event. All realizations were further downscaled with the regional weather prediction model, AROME-MetCoOp. The events were thereafter used as input to the operational Norwegian flood-forecasting model for 37 selected catchments in western Norway, and the magnitude and the spatial pattern of floods were analyzed. The role of the hydrological initial conditions, which are important for the total flooding, were analyzed with a special emphasis on snow and soil moisture excess. The results show that the selected future extreme precipitation events affected more catchments with larger floods, compared to the events from present climate. In addition, multiple realizations of the meteorological forcing and four different hydrological initial conditions, for example, soil saturation and snow storage, were important for the estimation of the maximum flood level. The meteorological forcing (e.g., the internal variability/perturbed output) accounts for the highest contribution to the spread in flood magnitude; however, for some events and catchments the hydrological initial conditions affected the magnitudes of floods more than the meteorological forcing. [ABSTRACT FROM AUTHOR]

In this study, precipitation- and temperature-related indices were considered using Rclimdex software to study trend of climate extreme indices. Daily minimum and maximum temperature data retrieved from MPI-ESM-LR and ACCESS1-0 global climate model were used to predict future climate extreme events over the next three periods of 2026–2050, 2051–2075, and 2076–2100 based on IPCC scenarios of RCP4.5 and RCP8.5 of studied area covering South Khorasan province and southern part of Razavi Khorasan province, located in east of Iran; furthermore, future saffron yield was predicted based on the yield-extreme indices model. Results showed an increasing trend of warm climate extreme indices and a decreasing trend in precipitation indices as important factors in the decrease of saffron yield. Considering multiple regression yield model of saffron based on climate extreme indices, it was concluded that saffron yield decreases in future periods over studied area with the highest reduction of 31% in period of 2076–2100 under RCP8.5 scenario. Results also confirmed that yield reduction in all three periods under RCP8.5 scenario was greater than the same periods under RCP 4.5 scenario. [ABSTRACT FROM AUTHOR]

The quantification of climate change impacts on hydrology is subjected to multiple uncertainty sources. Large ensembles of hydrological simulations based on multimodel ensembles (MMEs) have been commonly applied to represent overall uncertainty of hydrological impacts. However, as increasing numbers of global climate models (GCMs) are being developed, how many GCMs in MMEs are sufficient to characterize overall uncertainty is not clear. Therefore, this study investigates the influences of GCM quantity on quantifying overall uncertainty and uncertainty contributions of multiple sources in hydrological impacts. Large ensembles of hydrological simulations are obtained through the permutation of 3 greenhouse gas emission scenarios, 22 GCMs, 6 downscaling techniques, 5 hydrological models (HMs), and 5 sets of HM parameters, which enables to decompose uncertainty components using analysis of variance. The influences of GCM quantity are investigated by repeatedly conducting uncertainty decomposition for hydrological simulations from subsets with different numbers of GCMs. The results show that GCMs are the leading uncertainty sources in evaluating changes in annual and peak streamflows, while for changes in low flow, other uncertainty sources except HM parameters also have large contributions to overall uncertainty. Furthermore, on the condition of using no more than five GCMs, there are large possibilities that the overall uncertainty and GCMs' uncertainty contribution are underestimated. Using around 10 GCMs can ensure that the median of different combinations generates similar uncertainty components as the whole ensemble. Therefore, it is recommended to use at least 10 GCMs in studies of climate change impacts on hydrology to thoroughly quantify uncertainty. Plain Language Summary: Simulating hydrological responses to climate change involves multiple steps, which contribute uncertainty. Although climate model simulations are often found to be one of the major sources of uncertainty, how many simulations are needed to thoroughly estimate the uncertainty of hydrological responses is still not clear. Facing the growing number of climate simulations, using all of them will also pose a large computational burden in studying hydrological impacts of climate change. Therefore, we proposed a framework to evaluate how the numbers of climate simulations affect the quantification of uncertainty. Our results show that using insufficient numbers of global climate models leads to the underestimation of uncertainty. It is recommended to use at least 10 global climate models to adequately consider the uncertainty resulting from climate models. Our findings are helpful in the selection of sufficient climate simulations in simulating hydrological responses under climate change. Key Points: A framework to quantify the uncertainty contribution of GCMs over multiple sources in hydrological impacts of climate change is proposedUsing an insufficient number of GCMs may result in the underestimation of overall uncertainty and GCMs' contribution to uncertaintyIt is recommended to use at least 10 GCMs in studies of hydrological climate change impacts to thoroughly quantify uncertainty [ABSTRACT FROM AUTHOR]

Global climate models (GCMs) are generally used to forecast weather, understand the present climate, and project climate change. Their reliability usually rests on their capability to represent climatic processes, and most evaluations directly measure the spatiotemporal agreement of scalar climate variables. However, climate naturally involves complex interactions that are hard to infer and, therefore, difficult to evaluate. Climate networks (CNs) have been used to infer flows of mass and energy in the complex climate system. Here, an Evaluation of Models by Causal Flows (EMCaF) is proposed. EMCaF focuses on the assessment of properties about mass and energy flows in the CNs derived from GCMs. First, causal CNs are inferred from GCMs, and then the capabilities to reproduce characteristic transfer flows are assessed with reference models. A more in‐depth feature is the possibility to assess how climate change disturbs CNs properties. In addition to the quantitative difference between modelled and observed values taken into account in standard evaluations, the EMCaF approach aims to assess the weaknesses and strengths of GCMs to represent climate mechanisms and processes that couple different components of the climate system. The comparison of models through this approach allows having complimentary feedback on model evaluations to understand possible causes of errors and enable a judgement based on processes. The approach is illustrated by evaluating one GCM and subsequently assessing changes of its CNs under future climate projections. Results show that known climatic patterns are assimilated and that causal strength patterns are likely to agree with the wind magnitude as a transfer factor. Significative issues are then explored, showing the capabilities of the approach and allowing understand fundamental structures in transport flows, compare their properties, and assess changes in the future. Different alternatives and considerations in each step of the approach are discussed to expand its applicability. [ABSTRACT FROM AUTHOR]

The study assesses the performance of 24 model runs from five COordinated Regional climate Downscaling Experiment (CORDEX) regional climate models (RCMs) in simulating East Africa's spatio-temporal precipitation characteristics using a set of eight descriptors: consecutive dry days (CDD), consecutive wet days (CWD), simple precipitation intensity index (SDII), mean daily annual (pr_ANN), seasonal (pr_MAM and pr_OND) precipitation, and representatives of heavy precipitation (90p) and very intense precipitation (99p) events. Relatively better performing RCM runs are then used to assess projected precipitation changes (for the period 2071–2099 relative to 1977–2005) over the study domain under the representative concentration pathway (RCP) 8.5 scenario. The performance of RCMs is found to be descriptor and scope specific. Overall, RCA4 (r1i1p1) forced by CNRM-CERFACS-CNRM-CM5 and MPI-M-MPI-ESM-LR, REMO2009 (r1i1p1) forced by MPI-M-MPI-ESM-LR, and RCA4 (r2i1p1) forced by MPI-M-MPI-ESM-LR emerge as the top four RCM runs. We show that an ensemble mean of the top four model runs outperforms an ensemble mean of 24 model simulations and ensemble means for all runs in an RCM. Our analysis of projections shows a reduction (increase) in mean daily precipitation for MAM(OND), an increase(decrease) in CDD(CWD) events, and a general increase in SDII and the width of the right tail of the precipitation distribution (99p–90p). An increase in SDII and 99p–90p implies a possibility of occurrence of heavy and extreme precipitation incidences by the end of the twenty-first century. Our findings provide important information to support the region's climate change adaptation and mitigation efforts. [ABSTRACT FROM AUTHOR]

We quantify the skill of Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models to represent daily temperature extremes. We find CMIP models systematically exaggerate the magnitude of daily temperature anomalies for both cold and hot extremes. We assess the contribution to a daily temperature extreme from four terms: the long‐term mean annual cycle, the diurnal cycle, synoptic variability, and seasonal variability for both cold and hot extremes. These four terms are combined, and the overall performance of individual climate models assessed. This identifies those models that can simulate temperature extremes well and simulate them well for the right reasons. The new error metric shows that increases in horizontal resolution usually lead to a better performance particularly for the coarser resolution models. The CMIP6 improvements relative to CMIP5 are systematic across most land regions and are only partially explained by the increase in horizontal resolution, and other differences must therefore help explain the higher CMIP6 skill. Key Points: CMIP5 and CMIP6 models exaggerate the magnitude of daily temperature anomalies for hot days and cold nights extremesHigher‐resolution models improve the simulation of temperature extremes largely due to better simulation of synoptic scalesCMIP6 outperforms the simulation of temperature extremes compared to CMIP5 beyond the benefits given by the higher resolution [ABSTRACT FROM AUTHOR]

Finer grids in global climate models could lead to an improvement in the simulation of precipitation extremes. We assess the influence on model performance of increasing spatial resolution by evaluating pairs of high‐ and low‐resolution forced atmospheric simulations from six global climate models (generally the latest CMIP6 version) on a common 1° × 1° grid. The differences in tuning between the lower and higher resolution versions are as limited as possible, which allows the influence of higher resolution to be assessed exclusively. We focus on the 1985–2014 climatology of annual extremes of daily precipitation over global land, and models are compared to observations from different sources (i.e., in situ‐based and satellite‐based) to enable consideration of observational uncertainty. Finally, we address regional features of model performance based on four indices characterizing different aspects of precipitation extremes. Our analysis highlights good agreement between models that precipitation extremes are more intense at higher resolution. We find that the spread among observations is substantial and can be as large as intermodel differences, which makes the quantitative evaluation of model performance difficult. However, consistently across the four precipitation extremes indices that we investigate, models often show lower skill at higher resolution compared to their corresponding lower resolution version. Our findings suggest that increasing spatial resolution alone is not sufficient to obtain a systematic improvement in the simulation of precipitation extremes, and other improvements (e.g., physics and tuning) may be required. Key Points: Models generally agree on an intensification of precipitation extremes at higher spatial atmospheric resolutionObservational uncertainties are substantial for precipitation extremes, which makes the evaluation of the models difficultIncreasing spatial resolution alone is not sufficient to obtain a systematic improvement in the simulation of precipitation extremes [ABSTRACT FROM AUTHOR]

Rainfall events largely control hydrological processes occurring on and in the ground, but the performance of climate models in reproducing rainfall events has not been investigated enough to guide selection among the models when making hydrological projections. We proposed to compare the durations, intensities, and pause periods, as well as depths of rainfall events when assessing the accuracy of general circulation models (GCMs) in reproducing the hydrological characteristics of observed rainfall. We also compared the sizes of design storm events and the frequency and severity of drought to demonstrate the consequences of GCM selection. The results show that rainfall and extreme hydrological event projections could significantly vary depending on climate model selection and weather stations, suggesting the need for a careful and comprehensive evaluation of GCM in the hydrological analysis of climate change. The proposed methods are expected to help to improve the accuracy of future hydrological projections for water resources planning. [ABSTRACT FROM AUTHOR]

Over the past decades, significant changes in temperature and precipitation have been observed, including changes in the mean and extremes. It is critical to understand the trends in hydroclimatic extremes and how they may change in the future as they pose substantial threats to society through impacts on agricultural production, economic losses, and human casualties. In this study, we analyzed projected changes in the characteristics, including frequency, seasonal timing, and maximum spatial and temporal extent, as well as severity, of extreme temperature and precipitation events, using the severity–area–duration (SAD) method and based on a suite of 37 climate models archived in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Comparison between the CMIP5 model estimated extreme events and an observation-based dataset [Princeton Global Forcing (PGF)] indicates that climate models have moderate success in reproducing historical statistics of extreme events. Results from the twenty-first-century projections suggest that, on top of the rapid warming indicated by a significant increase in mean temperature, there is an overall wetting trend in the Northern Hemisphere with increasing wet extremes and decreasing dry extremes, whereas the Southern Hemisphere will have more intense wet extremes. The timing of extreme precipitation events will change at different spatial scales, with the largest change occurring in southern Asia. The probability of concurrent dry/hot and wet/hot extremes is projected to increase under both RCP4.5 and RCP8.5 scenarios, whereas little change is detected in the probability of concurrent dry/cold events and only a slight decrease of the joint probability of wet/cold extremes is expected in the future. [ABSTRACT FROM AUTHOR]

Projected changes in climatic extremes, compared to the mean climate, exhibit a greater negative impact on the natural environment. Several studies reported that multi-model ensemble approach can improve the reliability of hydro-climatic extreme projection by extracting important information from a large number of general circulation models (GCMs). However, most of the available multi-model assembling methods do not consider both the spatial and temporal variabilities. Thus, this study reflects both the spatial and temporal climate characteristics during multi-model averaging through the Taylor diagram skill metrics. The capability of the proposed multi-model assembling approach was evaluated for reproducing the multitude of climate extreme indices. Moreover, the reliability of a multi-model assembling approach was assessed for preserving the maximum variability of the GCMs output. In general, the results showed that multi-model assembling approach outperformed the individual climate models for reproducing the hydro-climatic extremes; however, it artificially corrupted and narrowed the projected climate extremes variability of the GCMs output. Thus, it is worthwhile to consider both the individual climate models and multi-model ensemble projections toward an improved projection of hydro-climatic extremes. In general, the study proved that the impacts of climate change on the hydro-climatic extremes are more amplified compared to the changes in mean climate. Hence, this study suggests that meaningful efforts should be put in the future to proactively manage the risks of climate extremes. [ABSTRACT FROM AUTHOR]

The present study describes the atmospheric component of the sixth‐generation climate models of the Centre National de Recherches Météorologiques (CNRM), namely, ARPEGE‐Climat 6.3. It builds up on more than a decade of model development and tuning efforts, which led to major updates of its moist physics. The vertical resolution has also been significantly increased, both in the boundary layer and in the stratosphere. ARPEGE‐Climat 6.3 is now coupled to the new version (8.0) of the SURFace EXternalisée (SURFEX) surface model, in which several new features (e.g., floodplains, aquifers, and snow processes) improve the water cycle realism. The model calibration is discussed in depth. An amip‐type experiment, in which the sea surface temperatures and sea ice concentrations are prescribed, and following the CMIP6 protocol, is extensively evaluated, in terms of climate mean state and variability. ARPEGE‐Climat 6.3 is shown to improve over its previous version (5.1) by many climate features. Major improvements include the top‐of‐atmosphere and surface energy budgets in their various components (shortwave and longwave, total and clear sky), cloud cover, near‐surface temperature, precipitation climatology and daily‐mean distribution, and water discharges at the outlet of major rivers. In contrast, clouds over subtropical stratocumulus decks, several dynamical variables (sea level pressure, 500‐hPa geopotential height), are still significantly biased. The tropical intraseasonal variability and diurnal cycle of precipitation, though improved, remained area of concerns for further model improvement. New biases also emerge, such as a lack of precipitation over several tropical continental areas. Within the CMIP6 context, ARPEGE‐Climat 6.3 is the atmospheric component of CNRM‐CM6‐1 and CNRM‐ESM2‐1. Plain Language Summary: Since the early 1990s, the Centre National de Recherches Météorologiques (CNRM) has been developing a global atmosphere model for climate applications. The present work presents its latest version, ARPEGE‐Climat 6.3, as prepared for the sixth phase of the Coupled Model Intercomparison Project (CMIP6). It builds up on more than a decade of model development and tuning efforts. A CMIP6 amip‐type numerical experiment, in which the sea surface temperatures and sea ice concentrations are prescribed, is evaluated, in terms of climate mean state and variability. ARPEGE‐Climat 6.3 is shown to have better or similar skills compared to its previous version and to rank rather high among CMIP5 state‐of‐the‐art models by many mean‐state metrics. Major improvements include the top‐of‐atmosphere and surface energy budgets, cloud cover, near‐surface temperature, precipitation climatology and daily‐mean distribution, and water discharges at the outlet of major rivers. In contrast, clouds over the eastern part of ocean basins, and a few dynamical variables, such as sea level pressure, are still significantly biased. New biases also emerge, such as a lack of precipitation over several tropical continental areas. The remaining and new biases call for further understanding, especially whether they arise from calibration issues or model structural limits. Key Points: Version 6.3 of the ARPEGE‐Climat atmospheric model includes an increased vertical resolution and a major update of the moist physicsImprovements include radiation, cloud and precipitation climatology, daily rainfall distribution, and water discharge at major river outletsWeaknesses still include biases in low clouds and some dynamical fields, while the West African monsoon is a new model deficiency [ABSTRACT FROM AUTHOR]

Derived from realistic global warming scenarios, long‐term projections of spatial patterns in precipitation and temperature in hydrology and climatology can serve to evaluate climate risk, explore sources of renewable energies and allow local‐scale data to inform decisions regarding agricultural, ecosystem, social, recreational and economic activities. Under the CORDEX‐EA project, the precipitation and temperature projections (2020–2045) for the economically and socially important region of Northwestern China were derived from high‐resolution regional climate model (RCM) simulations for RCP 4.5 and 8.5 scenarios, and compared against a historical period or baseline of 1980–2005. Drawing data from four key RCMs [Weather Research and Forecasting (WRF); Regional Spectral Model (RSM); Regional Climate Model version 4.0 (RegCM4); Mesoscale Model version 5 (MM5)], reliable local‐scale projections were generated by applying suitable bias correction that accords with the multivariate bias correction (MBC) approach. To validate this approach and then evaluate climate change impacts, the adjusted precipitation and temperature estimated from bias‐corrected models for the historical period were compared to the observed data. The results showed that the simulated spatiotemporal distribution of multiyear average precipitation and temperature appear to fit relatively well with the observations, however, the wet‐cold and dry‐warm climate‐related biases were still evident for the high altitude regions. Bias‐corrected future projections of RCMs indicated spatially averaged annual precipitation is expected to rise by about 23.6 and 35.3 mm under the RCP 4.5 and 8.5 scenarios, respectively, while spatially averaged annual temperature is expected to rise by about 1.95 and 1.10°C. Precipitation is projected to increase in all seasons, albeit, more so in the cold season (i.e., boreal winter and spring) than the warm season (i.e., boreal summer and autumn). The annual increase is expected to be about 56.7% under the RCP 4.5 scenario, compared to 67.6% under the RCP 8.5 scenario. The changes of mean temperature in winter are expected to be significant, −37.8% under both RCP scenarios. For spring, the mean temperature will rise by 23.1% (28.9%) under the RCP 4.5 (8.5) scenario. A MBC approach was found to be effective in yielding reliable projected changes in precipitation and temperature variables. The proposed approach has important implications for climatological studies and evaluation of climate change impacts on localized regions, not only in China, but also in other similar areas of the world, where decisions for managing climate risk must be implemented by policy makers, government, industry and stakeholders. [ABSTRACT FROM AUTHOR]

This study used a multi‐model ensemble (MME) simulation to project changes in extreme precipitation (EP) over the next century based on four relevant indexes (R95P, FRE, total precipitation [PRCPTOT] and PPER) during the monsoon period (May–September) over eastern Asia. The ensemble model was built from three global climate models from the Coupled Model Inter‐comparison Project Phase 5. The results indicate that the MME results can reproduce observations of the spatial distribution characteristics of EP indexes in eastern Asia. Compared with the reference period of 1986–2005, the EP (R95P), PRCPTOT and the ratio of EP to PRCPTOT (PPER) in eastern Asia will increase during the monsoon period under the three emission scenarios in three durations (the early period: 2020–2039, middle period: 2050–2069 and late period: 2080–2099) throughout the 21st century. This feature is especially strong in southeast China and the Malay Islands. Meanwhile, the variation in the frequency of EP events (FRE) has prominent regional differences, including a decrease in the Yellow River Basin and the Indochina Peninsula and an increase in southeast China. All projected increasing were most significant under high emissions (RCP8.5). This study also suggests that EP would not generally increase in frequency but rather in intensity. With an increase in emissions concentration, the temperature threshold at which EP occurs is likely to rise, so the EP might need triggered by the higher temperatures. [ABSTRACT FROM AUTHOR]

This study explores the updraft and downdraft properties of mature stage mesoscale convective systems (MCSs) in terms of draft core width, shape, intensity, and mass flux characteristics. The observations use extended radar wind profiler (RWP) and surveillance radar data sets from the U.S. Department of Energy Atmospheric Radiation Measurement program for midlatitude (Oklahoma, USA) and tropical (Amazon, Brazil) sites. MCS drafts behave qualitatively similar to previous aircraft and RWP cloud summaries. The Oklahoma MCSs indicate larger and more intense convective updraft and downdraft cores, and greater mass flux than Amazon MCS counterparts. However, similar size‐intensity relationships and draft vertical profile behaviors are observed for both regions. Additional similarities include weak positive correlations between core intensity and core width (correlation coefficient r ∼ 0.5) and increases in draft intensity with altitude. A model‐observational intercomparison for draft properties (core width, intensity, and mass flux) is also performed to illustrate the potential usefulness of statistical observed draft characterizations. Idealized simulations with the Weather Research and Forecasting model aligned with midlatitude MCS conditions are performed at model grid spacings (△x) that range from 4 km to 250 m. It is shown that the simulations performed at △x = 250 m at similar mature MCS lifecycle stages are those that exhibit draft intensity, width, mass flux, and shape parameter performances best matching with observed properties. Key Points: Mature Oklahoma mesoscale convective systems exhibit more intense, wider convective drafts than Amazon systemsDrafts intensity increases with core width and with altitudeSmaller model grid spacing simulations (Δx < 1 km) better capture observed draft intensity, width, shape, and mass flux [ABSTRACT FROM AUTHOR]

Increased knowledge of future changes in rainfall variability is needed to reduce vulnerability to potential impacts of global warming, especially in highly vulnerable regions like West Africa. While changes in mean and extreme rainfall have been studied extensively, rainfall variability has received less attention, despite its importance. In this study, future changes in West African summer monsoon (WASM) rainfall variability were investigated using data from two regional climate models that participated in the Coordinated Regional Climate Downscaling Experiment (CORDEX). The daily rainfall data were band-pass filtered to isolate variability at a wide range of timescales. Under global warming, WASM rainfall variability is projected to increase by about 10–28% over the entire region and is remarkably robust over a wide range of timescales. We found that changes in mean rainfall significantly explain the majority of intermodel spread in projected WASM rainfall variability. The role of increased atmospheric moisture is examined by estimating the change due to an idealized local thermodynamic enhancement. Analysis reveals that increased atmospheric moisture with respect to warming following the Clausius–Clapeyron relationship can explain the majority of the projected changes in rainfall variability at all timescales. [ABSTRACT FROM AUTHOR]

This study evaluated the performance of 25 earth system models (ESMs), statistically and dynamically downscaled to a high horizontal resolution (0.25° of latitude/longitude), in simulating extreme climate indices of temperature and precipitation for 1980–2005. Datasets analyzed include 21 statistically downscaled ESMs from the National Aeronautics and Space Administration (NASA) Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) and dynamically downscaled Eta Regional Climate Model simulations driven by 4 ESMs generated by the Brazilian National Institute for Space Research (INPE). Downscaled outputs were evaluated against observational gridded datasets at 0.25° resolution over Brazil, quantifying the skill in simulating the observed spatial patterns and trends of climate extremes. Results show that the downscaled products are generally able to reproduce the observed climate indices, although most of them have poorest performance over the Amazon basin for annual and seasonal indices. We find larger discrepancies in the warm spell duration index for almost all downscaled ESMs. The overall ranking shows that three downscaled models (CNRM-CM5, CCSM4, and MRI-CGCM3) perform distinctively better than others. In general, the ensemble mean of the statistically downscaled models achieves better results than any individual models at the annual and seasonal scales. This work provides the largest and most comprehensive intercomparison of statistically and dynamically downscaled extreme climate indices over Brazil and provides a useful guide for researchers and developers to select the models or downscaling techniques that may be most suitable to their applications of interest over a given region. [ABSTRACT FROM AUTHOR]

Global mean temperature is continuously rising and causing changes in the extreme climatic events. Following these changes, climate extremes—the rare events that reside in the tail of the distribution of essential climate variables—are expected to be further intensified, more frequent, and prolonged. Changes in extremes would vary spatially from region to region and thus need regional assessment for future adaptation planning. This study assesses the climate extremes at 1.5 °C, 2 °C, and 4 °C of global warming over Bangladesh which is one of the most vulnerable countries to climate change. Future changes in climate extremes are assessed using a subset of extreme temperature and precipitation indices devised by Expert Team on Climate Change Detection and Indices (ETCCDI). Projections from high-resolution regional climate model ensembles are used to derive extreme climate indices. Our analysis shows overall upward changes in warm indices and downward changes in cold indices at higher specific warming levels. We found a much higher increase in extreme rainfall compared with the annual total rainfall. Increasing variability of rainfall indices is found at higher specific warming levels. Our analysis also suggests a higher increase of temperature during the winter and post-monsoon seasons, as well as an increase in the 1-day and 5-day maximum rainfall during pre- and post-monsoon seasons. A significant regional difference is found in almost all the rainfall indices. The forecasted increase of extreme rainfall and consecutive dry days (CDD) over the northeast region indicates a possibility of an increase of flash floods in the future. Moreover, the increase in the extreme rainfall over the southeast region will increase the chances of landslides. [ABSTRACT FROM AUTHOR]

As the world overheats—potentially to conditions warmer than during the three million years over which modern humans evolved—suffering from heat stress will become widespread. Fundamental questions about humans' thermal tolerance limits are pressing. Understanding heat stress as a process requires linking a network of disciplines, from human health and evolutionary theory to planetary atmospheres and economic modeling. The practical implications of heat stress are equally transdisciplinary, requiring technological, engineering, social, and political decisions to be made in the coming century. Yet relative to the importance of the issue, many of heat stress's crucial aspects, including the relationship between its underlying atmospheric drivers—temperature, moisture, and radiation—remain poorly understood. This review focuses on moist heat stress, describing a theoretical and modeling framework that enables robust prediction of the averaged properties of moist heat stress extremes and their spatial distribution in the future, and draws some implications for human and natural systems from this framework. ▪ Moist heat stress affects society; we summarize drivers of moist heat stress and assess future impacts on societal and global scales. ▪ Moist heat stress pattern scaling of climate models allows research on future heat waves, infrastructure planning, and economic productivity. [ABSTRACT FROM AUTHOR]

Extreme temperature events derived from the global climate models (GCMs) are used for climate change impact studies in several sectors, especially in agriculture. Reducing the uncertainty in simulated crop yields based on extreme temperatures is an important task for reliable and relevant adaptation decisions. This study compared the performance of selected six global climate models in simulating temperature extreme events over Indian region for the 1976–2005 period. For this, performance statistics such as root mean square error, correlation coefficient, and agreement index were compared spatially and spatiotemporally. The study reveals that all the six models overestimate minimum and maximum temperature extremes for most parts of Central India, which resulted in hot bias. However, these models show a cold bias in simulating low temperature extremes over the Himalayan region. Further the study indicates that GFDL-ESM2M and MIROC-ESM-CHEM have performed relatively better in capturing temperature extreme events over Indian region among the six models that were found to closely simulate the observed climatology. [ABSTRACT FROM AUTHOR]

The extreme monthly mean air and land surface temperatures simulated by the Beijing Climate Center (BCC) climate model from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) are evaluated and compared with those from the version for the fifth phase Coupled Model Intercomparison Project (CMIP5) at global mean, zonal mean, and regional mean scales. Comparisons with the European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) and the Global Land Data Assimilation System (GLDAS) datasets indicate that both the CMIP5 and CMIP6 versions of the BCC model can simulate the warm and cold extremes of monthly air and land surface temperatures reasonably well for the period of 1979–2005. Compared with reanalyses, the CMIP6 version has better consistencies in the magnitudes, narrower spreads of zonal extremes, and smaller root mean square errors (RMSEs) than the CMIP5 version due to an update with a new cloud fraction scheme and a snow-albedo scheme. When the annual climatological cycle is removed, the CMIP6 version performs better than the CMIP5 version on the interannual variability of both warm and cold extremes. Changes in the air and land surface temperature extremes for the CMIP6 version are more similar to the reanalyses. We found that the warming rates of both warm and cold extremes at regional scales are significantly higher than the global averaged warming rate of surface temperatures, especially at the high latitudes of the Northern Hemisphere, which is consistent with the previous studies on monthly mean air temperature. Further, both the BCC models and reanalyses show that the cold extremes warm faster than the warm extremes, and the land surface temperature extremes warm more quickly than the air temperature extremes during this period. [ABSTRACT FROM AUTHOR]

In this study, an analysis of present day climate simulation (1998–2008) is presented for the Central African (CA) region with the COnsortium for Small‐scale MOdelling in CLimate Mode (CCLM) regional climate model, forced by the ERA‐Interim (ERAINT) reanalysis data. The ability of the CCLM to simulate the observed precipitation with particular focus on the mean spatial pattern, low‐level circulation, seasonal cycles, and daily characteristics is evaluated. Likewise, the added value of the regional model CCLM compared to the driving ERAINT reanalysis is also investigated. It is shown that ERAINT and CCLM exhibit quite different sign of bias, which is an indication of the importance of internal variability and fine scale processes representation for the simulation of surface climate. Despite the CCLM is constantly dry over southern CA, the model succeeds to reproduce reasonably the mean spatial patterns of precipitation and low‐level circulation features, along with the associated seasonal cycles over the whole CA and majority of the five selected analysis sub‐regions. Results also show that daily precipitation indices are well represented, although the better performance greatly depends on the considered seasons. Nevertheless, CCLM substantially outperforms the ERAINT daily precipitation characteristics, thus highlighting the added value of the downscaling exercise over the region. The analysis of daily precipitation indices also reveals that the dry character of the model could probably be connected to the underestimation of the simulated less intense events, which in turn result to an overestimation of the simulated dry spell duration. [ABSTRACT FROM AUTHOR]

We assess the representation of multiday temperature and rainfall extremes in southeast Australia in three coupled general circulation models (GCMs) of varying resolution. We evaluate the statistics of the modeled extremes in terms of their frequency, duration, and magnitude compared to observations, and the model representation of the midtropospheric circulation (synoptic and large scale) associated with the extremes. We find that the models capture the statistics of observed heatwaves reasonably well, though some models are "too wet" to adequately capture the observed duration of dry spells but not always wet enough to capture the magnitude of extreme wet events. Despite the inability of the models to simulate all extreme event statistics, the process evaluation indicates that the onset and decay of the observed synoptic structures are well simulated in the models, including for wet and dry extremes. We also show that the large-scale wave train structures associated with the observed extremes are reasonably well simulated by the models although their broader onset and decay is not always captured in the models. The results presented here provide some context for, and confidence in, the use of the coupled GCMs in climate prediction and projection studies for regional extremes. [ABSTRACT FROM AUTHOR]

Present study attempts to project extreme precipitation indices over 34 different meteorological subdivisions and six homogeneous regions such as Northwest, Central Northeast, Northeast, West Central, Peninsular India and Hilly Region during summer monsoon season in the twenty-first century. For this purpose, the Regional Climate Model version4 (RegCM4) had been run at 50 km horizontal resolution forced with the global model GFDL-ESM2 M, during reference period 1976–2005 for the model validation, and the mid- (2031–2060) and far-future (2070–2099) for projections under RCP8.5 scenario over the South Asia CORDEX domain. In this paper, model simulated precipitation has been validated against IMD, APHRODITE and NCEP/NCAR data sets. The results indicate that RegCM4 captures the important features of seasonal precipitation and various extreme indices over the study area. The RegCM4 has projected an increase in the mean seasonal precipitation by 0.56 mm/day whereas in case of GFDL model the rate is 0.39 mm/day during the far-future relative to the reference period. The heavy precipitation indices are projected to increase more frequently (0.264/decade) than the mean precipitation rate (0.01/decade) over India. The correlations between the extreme precipitation indices and the seasonal mean precipitation are found to be strong. In addition, the consecutive dry days are projected to occur more frequently (3–5 days) over West Central (Telangana, Vidarbha and Marathwada) and west Rajasthan while consecutive wet days are projected to decrease over larger parts of India during far-future. Similarly, 1 day maximum precipitation and the simple daily intensity index are projected to increase consistently from mid- to far- futures over some sub-divisions of West coast, Hilly and Northeast regions. From a spatial probability perspective, model projection indicates more frequent severe drought and flood conditions over India. [ABSTRACT FROM AUTHOR]

This study aims to give a brief overview of an ensemble of regional climate model (RCM) simulations with and without bias correction for daily precipitation for the Carpathian Region located in Central/Eastern Europe. Within the international initiative called the Coordinated Regional Downscaling Experiment (CORDEX), EURO-CORDEX and Med-CORDEX provide RCM simulations targeting Europe as a whole or in a part at the grid resolutions of 0.44° (~50 km) and 0.11° (~12 km). The ensemble of RCMs provides a huge amount of data, which are, however, prone to biases compared to highresolution observations. First, the bias correction of the daily precipitation output of EUROCORDEX and Med-CORDEX RCM ensemble at a common 0.11° × 0.11° horizontal grid resolution is performed based on the high-resolution, high-quality observational dataset CARPATCLIM. The region covered by the CARPATCLIM dataset can be considered as the Carpathian Region, for which the RCM ensemble (consisting of six members in total at 0.11° resolution) of a historical period (1976-2005) and under the Representative Concentration Pathway 8.5 (RCP8.5) over two future time slices (2021-2050 and 2070-2099) are assessed. Percentile-based bias correction method was used in order to adjust systematic biases in all simulated precipitation fields. The present study focuses on different precipitation climate indices derived from high-resolution RCM outputs over the entire Carpathian Region and specifically two sub-regions representing high- and lowlands within the target region. The analyzed indices are as follows: the frequency of rainy days (RR1, days with a total rainfall of at least 1 mm), heavy precipitation days (RR10, days with a total rainfall of at least 10 mm), highest daily precipitation (RX1), maximum consecutive dry periods (CDD, the duration of the longest period with < 1 mm total daily precipitation), maximum consecutive wet periods (CWD, the duration of the longest period with > 1 mm total daily precipitation). Our results indicate that both the spatial distribution and magnitude of mean changes are similar to those found in previous works based on ENSEMBLES project simulations using a different greenhouse gas emission scenario. Furthermore, the present study also aims to introduce a high-resolution bias-corrected precipitation database, which can serve as input for climate change impact and adaptation studies to be carried out for the Carpathian Region and to provide important information to stakeholders and decision makers at local/regional/national levels. [ABSTRACT FROM AUTHOR]

A challenge for climate impact studies is the selection of a limited number of climate model projections among the dozens that are typically available. Here, we examine the impacts of methods for climate model selection on projections of runoff change for five different watersheds across the conterminous USA. The results from an ensemble of 29 global climate models and 29 corresponding hydrological model simulations are compared with the results that would have been obtained by applying six different selection methods to the climate model data and using only the selected models to drive the hydrological model. We evaluate each selection method based on whether the runoff projections produced by the method meet the method's objective and on whether the results are sensitive to the number of models chosen. The Katsavounidis–Kuo–Zhang (KKZ) method, which is intended to maximize the spread in the projected climate change, was the only method that met both criteria for suitability. Although the KKZ method generally performed well, the results from both it and the other methods varied somewhat unpredictably based on region and number of models chosen. This study shows that the methods and models used in similar top–down studies should be carefully chosen and that the results obtained should be interpreted with caution. [ABSTRACT FROM AUTHOR]

Flooding is one of the main natural hazards in the world and causes huge economic and human impacts. Assessing the flood damage in the Mediterranean region is of great importance, especially because of its large vulnerability to climate change. Most past floods affecting the region were caused by intense precipitation events; thus the analysis of the links between precipitation and flood damage is crucial. The main objective of this paper is to estimate changes in the probability of damaging flood events with global warming of 1.5, 2 and 3 ∘ C above pre-industrial levels and taking into account different socioeconomic scenarios in two western Mediterranean regions, namely Catalonia and the Valencian Community. To do this, we analyse the relationship between heavy precipitation and flood-damage estimates from insurance datasets in those two regions. We consider an ensemble of seven regional climate model (RCM) simulations spanning the period 1976–2100 to evaluate precipitation changes and to drive a logistic model that links precipitation and flood-damage estimates, thus deriving statistics under present and future climates. Furthermore, we incorporate population projections based on five different socioeconomic scenarios. The results show a general increase in the probability of a damaging event for most of the cases and in both regions of study, with larger increments when higher warming is considered. Moreover, this increase is higher when both climate and population change are included. When population is considered, all the periods and models show a clearly higher increase in the probability of damaging events, which is statistically significant for most of the cases. Our findings highlight the need for limiting global warming as much as possible as well as the importance of including variables that consider change in both climate and socioeconomic conditions in the analysis of flood damage. [ABSTRACT FROM AUTHOR]

Multimodel combining approaches can extract reliable climate information from a large number of climate projections by exploiting the strengths and discounting the weaknesses of each climate simulator; however, most of them (e.g., reliability ensemble averaging [REA]) assign weights to climate simulators without accounting for spatial and temporal variabilities in climate model skills. Here we tested several REAs and proposed a full version that reflects the spatiotemporal (ST) variability of climate model skills. Interperformance evaluations between REA versions showed that, on average, ST‐REA reduced the bias by 33.78% and the root mean square error by 11.61%. Therefore, spatial and temporal variabilities in climate model skills can enhance the overall reliability of precipitation projections. ST‐REA was applied to project future precipitations over South Korea by combining seven climate models: The spatially averaged projected changes were 2.77%, 8.15%, and 7.58% for the 2020–2039, 2040–2069, and 2070–2099 periods, respectively. Plain Language Summary: The spatiotemporal variability of climate characteristics is one of the most pronounced climate behaviors. Thus, considering spatiotemporal variabilities during the assessment of climate models' skills when performing a multimodel averaging may improve the overall reliability of the multimodel ensemble estimate. Nevertheless, most of the available multimodel averaging approaches (e.g., the REA) do not consider in parallel the spatial and temporal variabilities during climate model weighting. In this study, we presented an improved version of the REA, which can characterize the spatiotemporal variabilities at multiple sites and time steps during multimodel averaging. The proposed REA versions have improved weight assignation mechanisms (in respect to each climate simulator) and can handle multiple weather stations. Key Points: Three augmented versions of reliability ensemble averaging were proposed to achieve reliable precipitation projections over South KoreaThe spatiotemporal variability of climate model skills within a multimodel approach improved the overall reliability of precipitation projectionsThe spatiotemporal reliability ensemble averaging outperformed all versions of reliability ensemble averaging in precipitation projection [ABSTRACT FROM AUTHOR]

Projections of seasonal extreme precipitation changes in eight Mediterranean subregions between the end of the twentieth and the end of the twenty-first century are analyzed using weighted multi-model ensembles. Weights are based on the performance of predictor variables in the scope of statistical downscaling. Two indices of precipitation scarcity as well as two indices of heavy precipitation are downscaled from global climate model data of the Coupled Model Intercomparison Project phase 3 and 5 (CMIP3, CMIP5) multi-model ensembles, considering two emission scenarios each. Based on the performance with regard to observations of extreme precipitation as well as inter-model consistency, three weighting metrics are calculated and subsequently applied to each ensemble. While meteorological droughts are projected to increase in most cases, the tendency is less pronounced for heavy precipitation events and mostly points towards reduction. The weighting does not affect the multi-model mean changes, but induces a decrease of ensemble spread (although mostly not significant), implying a decrease of model uncertainty. As the ensemble and scenario considered have minor effect on the findings and also the differences between seasons and subregions are not marked, there is strong evidence for enhanced droughts in the Mediterranean region, implying major socio-economic and ecological consequences. [ABSTRACT FROM AUTHOR]

The veracity of urban climate simulation models should be systematically evaluated to demonstrate the trustworthiness of these models against possible model uncertainties. However, existing studies paid insufficient attention to model evaluation; most studies only provided some simple comparison lines between modelled variables and their corresponding observed ones on the temporal dimension. Challenges remain since such simple comparisons cannot concretely prove that the simulation of urban climate behaviours is reliable. Studies without systematic model evaluations, being ambiguous or arbitrary to some extent, may lead to some seemingly new but scientifically misleading findings. To tackle these challenges, this article proposes a methodological framework for the model evaluation of high-resolution urban climate simulations and demonstrates its effectiveness with a case study in the area of Shenzhen and Hong Kong SAR, China. It is intended to (again) remind urban climate modellers of the necessity of conducting systematic model evaluations with urban-scale climatology modelling and reduce these ambiguous or arbitrary modelling practices. [ABSTRACT FROM AUTHOR]

We employ a large ensemble of Regional Climate Models (RCMs) from the COordinated Regional-climate Downscaling EXperiment to explore two questions: (1) what can we know about the future precipitation characteristics over Africa? and (2) does this information differ from that derived from the driving Global Climate Models (GCMs)? By taking into account both the statistical significance of the change and the models' agreement on its sign, we identify regions where the projected climate change signal is robust, suggesting confidence that the precipitation characteristics will change, and those where changes in the precipitation statistics are non-significant. Results show that, when spatially averaged, the RCMs median change is usually in agreement with that of the GCMs ensemble: even though the change in seasonal mean precipitation may differ, in some cases, other precipitation characteristics (e.g., intensity, frequency, and duration of dry and wet spells) show the same tendency. When the robust change (i.e., the value of the change averaged only over the land points where it is robust) is compared between the GCMs and RCMs, similarities are striking, indicating that, although with some uncertainty on the geographical extent, GCMs and RCMs project a consistent future. Potential added value of downscaling future climate projections (i.e., non-negligible fine-scale information that is absent in the lower resolution simulations) is found for instance over the Ethiopian highlands, where the RCM ensemble shows a robust decrease in mean precipitation in contrast with the GCMs results. This discrepancy may be associated with the better representation of topographical details that are missing in the large scale GCMs. The impact of the heterogeneity of the GCM–RCM matrix on the results has been also investigated; we found that, for most regions and indices, where results are robust or non-significant, they are so independently on the choice of the RCM or GCM. However, there are cases, especially over Central Africa and parts of West Africa, where results are uncertain, i.e. most of the RCMs project a statistically significant change but they do not agree on its sign. In these cases, especially where results are clearly clustered according to the RCM, there is not a simple way of subsampling the model ensemble in order to reduce the uncertainty or to infer a more robust result. [ABSTRACT FROM AUTHOR]

The anthropogenic‐induced global warming and local urbanization exert important influences on temperature extremes in Eastern China. Here we use China station observations and climate models to investigate their effects on the warm and cold days and nights simultaneously. We quantified the contribution from these two factors based on an optimal fingerprinting method. We find that both anthropogenic and urbanization signals can be clearly detected and separated from each other in the nighttime temperature extremes. The effect of urbanization may explain as much as one third of the observed changes in cold and warm nights while the urbanization signal is weak in the daytime extremes. The results are robust against sampling uncertainty in the estimate of urbanization signal, but uncertainty due to collinearity between the urbanization signal and global warming is difficult to assess. Plain Language Summary: Understanding the causes behind changes in temperature extremes is of significance for reliably projecting future climate change. Previous studies have separately shown that global warming and the urbanization effects are the two important drivers for the increase of warm extremes and decrease of cold extremes in Eastern China. In this study, we consider these two factors simultaneously using an optimal fingerprinting method. We find that climate models can well reproduce the observed changes in extreme temperature when the urbanization effects are included. Both global warming and urbanization have contributed to changes in nighttime temperature extremes, with global warming contributing slightly more. On the other hand, changes in daytime temperature extremes seem to be predominantly due to global warming. Key Points: Global warming and urbanization exert important influences on both warm and cold days and nights in Eastern ChinaBoth anthropogenic and urban signals are detected in nighttime extreme temperatures while only the former is detected in daytime extremesThe effect of urbanization is estimated to have contributed as much as one third of the observed warming in nighttime temperature extremes [ABSTRACT FROM AUTHOR]

Climate models provide information that resource managers, policy makers, and researchers can use when planning for the future. While this information is valuable in the broad sense, the low spatial-resolution often lacks local details that resource managers and decision makers need to plan for their communities. Therefore, statistically downscaled climate projections provide a high-resolution output and offer local information that is more beneficial than coarse-resolution global climate model output. In the Red River Basin, located in the south-central U.S., this detailed information is used to develop long-term water plans. This area is prone to drought conditions and heavy precipitation events, and studies have consistently estimated that this will continue in the future. This paper introduces a dataset of statistically downscaled climate projections of daily minimum and maximum temperature and daily precipitation that is a useful tool for studies regarding climatological and hydrological aspects in the region. The dataset was created using two quantile mapping techniques to downscale the CCSM4, MPI-ESM-LR, and MIROC5 model outputs to a 0.1-degree spatial resolution. Furthermore, we describe the added value of coproduction of knowledge between climate scientists and end users, or in this case impacts modelers and decision makers, for creating climate projections that can be used for climate risk assessments. A case study of the data's development and application is provided, detecting the mean daily changes in temperature and precipitation through the end of the century in the Red River Basin for two representative concentration pathways. After applying the users' inputs to develop the datasets, results for this example estimate an increase in mean daily precipitation in the eastern portion of the basin and as much as a 15% decline in the west by the end of the century. Furthermore, mean daily temperature is expected to rise across the entire basin in all scenarios by up to 6–7°C. [ABSTRACT FROM AUTHOR]

The impacts of climate change on future drought properties in various regions across China are accessed using multiple statistical approaches, based on 20 downscaled global climate models provided by NASA (NEX-GDDP) under Representative Concentration Pathways (RCP) 4.5 and RCP8.5 emission scenarios. Results show that temperature plays a crucial role on the variability of drought conditions in China by comparing the discrepancies between Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). Unobvious variability of drought extent is projected for SPI, while the drought extent for SPEI is remarkable. Based on SPEI, a considerable aggravation in spatial extent and severity of future drought events are found in the majority of regions, particularly in northwest and northeast China, except for winter over northeast region. The drought extent increases more significantly after late 2070s under RCP8.5 scenario, and the differences of drought extent are not significant between RCP4.5 and RCP8.5 scenarios in the early and mid-twenty-first century. More than 85% of the regions show a decreasing trend for SPEI in spring, summer, and autumn, suggesting drought tendency in most of China, and drought frequency also increases significantly in north and northwest China except for winter. The dramatic aggravation of drought attribution is mainly projected to the increases in potential evapotranspiration (PET) in northwestern and northern regions of China, whereas in northwestern region, the exacerbating drought conditions are expected to the attribution of deficiencies of rainfall. At national scale, PET plays a more dominant role to the future severe and widespread droughts across China in the context of climate change. [ABSTRACT FROM AUTHOR]

Precipitation and temperature are physically related to each other with significant dependences that vary with seasons at regional and global scales. The dependence between precipitation and temperature (P–T dependence) plays a central role in characterizing the joint behaviour of the two variables. Along with extensive studies on the variation in the mean, variance, or extremes of precipitation and temperature under global warming, it is of particular interest to understand the climate‐change impacts on the covariability of precipitation and temperature. The aim of this study therefore is to assess climate change impacts on the P–T dependence over global land areas from observations, and the performance of the current‐generation Coupled Model Intercomparison Project (CMIP5) models in simulating these patterns. In general, the CMIP5 models correctly simulate the overall pattern of the P–T dependence (around 87–94% of global land areas). However, they capture both the sign and change of P–T dependence for relatively small regions (around 41–47% of global land areas). Results from this study may provide useful insights for the future development of climate models. [ABSTRACT FROM AUTHOR]

A statistical framework for evaluating changes in extreme events is proposed and applied to evaluate a 20-member, regional climate model ensemble simulation with 30-km resolution. The model is found to represent the statistics and distributions of extreme events, including observed wet day characteristics, wet/dry days, and wet/dry spell characteristics, reasonably across Africa. Simulations of the mid-twenty-first and late-twenty-first century project statistically-significant changes in these societally-relevant climate characteristics in three regions. Intensification of rainfall is projected for the Sahel rainy season, including large increases in wet spell frequency, wet spell duration, and wet spell intensity. These changes are statistically significant at mid-twenty-first century and become more spatially robust by the end of the century. A weaker intensified rainfall trend is also projected over East Africa, for northern Ethiopia in boreal summer, Tanzania in boreal winter, and southern Ethiopia, Somalia, and the Lake Victoria region in boreal fall. The changes are significant in scattered regions at mid-twenty-first century, but widespread and highly significant by the end of the century. In contrast, increased dry periods is projected for parts of southern Africa (Angola, Zambia, Malawi), including 50–100% reductions in wet spell frequency annually and increases in dry spell duration in austral spring. The spatial coherence and rigorous statistical analysis of the projected changes combined with their physical consistency with the findings of previous studies support confidence in these results. [ABSTRACT FROM AUTHOR]

Coarse‐resolution global climate models cannot explicitly resolve the intensity distribution of tropical precipitation and how it responds to a forcing. We use a cloud‐resolving model to study how imposed atmospheric radiative heating (such as that caused by greenhouse gases or absorbing aerosols) may alter precipitation intensity in the setting of radiative‐convective equilibrium. It is found that the decrease in total precipitation is realized through preferentially reducing weak events. The intensity of strong precipitation events is maintained by a cancellation between the moistening of air parcels and weakening of updrafts. A boundary layer energy budget analysis suggests that free‐tropospheric heating raises boundary layer temperatures mainly through a reduction in rain reevaporation. This insight leads to a predictive scaling for the surface sensible and latent flux changes. The results imply that cloud microphysical processes play a key role in shaping the temperature and precipitation responses to atmospheric heating. Key Points: Atmospheric heating preferentially reduces weak precipitationA cancellation between thermodynamic and dynamic effects maintains the intensity of strong precipitationFree‐tropospheric heating warms the boundary layer mainly through reducing rain reevaporation [ABSTRACT FROM AUTHOR]

Climate scenarios derived from the global climate models (GCMs) are used for climate change impact studies in several sectors including agriculture, hydrological, and health. Globally, more than 50 climate models exist and choosing suitable models based on reproducibility of observed weather for a study region is a challenging task. This step is important to reduce uncertainty. This study compared the simulation performance of 12 global climate models for temperatures and rainfall in past 30 years over Indian region. For this, Priority index from Fuzzy Analytic Hierarchy Process (FAHP) and Reliability index were used and both methods were compared. Study revealed all 12 models overestimated minimum and maximum temperatures in most regions of India, which resulted in hot bias especially in northern region. However, models showed significant cold bias for the Himalayan region. In general, simulated rainfall was underestimated by many GCMs. The analysis indicated that FAHP method is good to shortlist GCMs based on their spatial and temporal performance in reproducing observed weather. Among 12 models, NORESM1 model has performed better in reproducing maximum temperature. The IPSL-LR, FIO-ESM, GFDL-CM3, and MIROC5 models have performed better for minimum temperature. In case of rainfall, CSIRO, MIROC5, HADGEM2, GFDL-ESM 2 M, and IPSL-LR have performed better as compared to other models. [ABSTRACT FROM AUTHOR]

Choosing downscaling techniques is crucial in obtaining accurate and reliable climate change predictions, allowing for detailed impact assessments of climate change at regional and local scales. Traditional statistical methods are likely inefficient in downscaling precipitation data from multiple sources or complex data patterns, so using deep learning, a form of nonlinear models, could be a promising solution. In this study, we proposed to use deep learning models, the so‐called long short‐term memory and feedforward neural network methods, for precipitation downscaling for the Vietnamese Mekong Delta. Model performances were assessed for 2036–2065 period, using original climate projections from five climate models under the Coupled Model Intercomparison Project Phase 5, for two Representative Concentration Pathway scenarios (RCP 4.5 and RCP 8.5). The results exhibited that there were good correlations between the modelled and observed values of the testing and validating periods at two long‐term meteorological stations (Can Tho and Chau Doc). We then analysed extreme indices of precipitation, including the annual maximum wet day frequency (Prcp), 95th percentile of precipitation (P95p), maximum 5‐day consecutive rain (R5d), total number of wet days (Ptot), wet day precipitation (SDII) and annual maximum dry day frequency (Pcdd) to evaluate changes in extreme precipitation events. All the five models under the two scenarios predicted that precipitation would increase in the wet season (June–October) and decrease in the dry season (November–May) in the future compared to the present‐day scenario. On average, the means of multiannual wet season precipitation would increase by 20.4 and 25.4% at Can Tho and Chau Doc, respectively, but in the dry season, these values were projected to decrease by 10 and 5.3%. All the climate extreme indices would increase in the period of 2036–2065 in comparison to the baseline. Overall, the developed downscaling models can successfully reproduce historical rainfall patterns and downscale projected precipitation data. [ABSTRACT FROM AUTHOR]