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In December 2022, North America experienced an unprecedented extreme cold event. However, the underlying physical mechanisms of this cold wave, and the extent to which it is driven by internal variability or external forcing, are not fully understood. Using ERA5 reanalysis data and the HadGEM3‐A‐N216 attribution simulations, we identified internal variability as the main cause, contributing −5.14 K to surface air temperature (SAT) anomalies in North America. External forcing slightly mitigated the cold by 0.42 K. An internally generated wave train from the North Pacific, influenced in combination by Pacific‐North American (PNA) and North Pacific Oscillation (NPO) teleconnection patterns, initiated this intense cyclonic event, contributing −2.18 K and −2.12 K to SAT anomalies, respectively. La Niña‐like sea surface temperature anomalies amplified this wave train and resultant cold wave. Additionally, excessive snow cover in the previous November also intensified the December cold anomalies by enhancing surface albedo and reducing solar radiation. Plain Language Summary: In December 2022, North America was hit by an exceptionally severe cold event. Scientists have been trying to understand the reasons behind this cold wave. Using detailed weather data and climate models, researchers found that natural climate variability played a major role, causing temperatures to drop by about 5.14 degrees Celsius. On the other hand, external factors like human‐induced climate change had a minor effect, slightly reducing the severity of the cold by 0.42 degrees Celsius. The study identified an atmospheric wave train pattern, originating from the North Pacific and moving toward North America, which played a crucial role in triggering this extreme weather. Further investigations showed that this wave train was influenced by specific large‐scale weather patterns in the Pacific region, namely the Pacific‐North American (PNA) and North Pacific Oscillation (NPO) patterns, which contributed to the cold temperatures by approximately 2.18 and 2.12 degrees Celsius, respectively. Additionally, colder‐than‐normal sea surface temperatures in the tropical central Pacific, associated with La Niña, strengthened the wave train. Internal thermodynamical processes, such as increased snow cover, also slightly intensified the cold wave by reflecting more sunlight and reducing the amount of solar energy reaching the surface. Key Points: Internal variability triggers 2022 December extreme cold wave in North AmericaPNA and NPO are two key teleconnections responsible for this extreme cold waveSnow cover‐SAT feedback intensifies this extreme cold wave [ABSTRACT FROM AUTHOR]

The accurate partitioning of precipitation is important for snowpack and streamflow simulations in mountainous regions. However, most hydrologic models employ a partitioning method based on near‐surface air temperature (Ta) and the sensitivity of hydrologic simulations to the selection of the precipitation partitioning method (PPM) under climate change has been underexplored. To address this, we compared simulations using two PPMs, a conventional Ta‐based scheme and a wet‐bulb temperature (Tw)‐based approach (TA and TW) with the Variable Infiltration Capacity model. Our study focused on the Colorado River Basin (CRB) which is vulnerable to future snowfall and streamflow reductions due to climate warming. Historical simulations demonstrated the improved performance of the TW scheme in simulating snowfall fraction (SF) and snow water equivalent (SWE) in relation to in situ observations and a gridded SWE product. Subsequently, we evaluated the influence of the PPM selection on snowpack and streamflow projections from eight climate models under two emissions scenarios. The ensemble median results revealed more substantial decline in annual SF with the TW scheme as compared to the TA method (−12% vs. −9%) from the historical (1976–2005) to the far future (2066–2095) period, especially at lower elevations. Hydrologic simulations showed that the TA scheme underestimated reductions in SWE and streamflow (Q) under warming as compared to the TW scheme. This finding has important implications on future projections for the CRB and in other mountainous basins where climate warming can shift conditions from snowfall to rainfall. Key Points: The precipitation partitioning method (PPM) impacts the simulation of snowfall and snowpack dynamics in the Colorado River Basin (CRB)The selection of PPM also determines the sensitivity of streamflow projections in the CRB under climate change scenariosSpatial variations in the impact of PPM reveal distinct climate change sensitivities in high and low elevation regions in the CRB [ABSTRACT FROM AUTHOR]

To evaluate future changes in the climate system, the outputs from coarse‐resolution global climate models (GCMs) need to be downscaled to a finer scale, making them more directly applicable for impact assessment. Here we focus on examining the projected changes of three key climate variables (precipitation, air temperature, and wet bulb temperature) across Arizona (south‐western United States). We use daily outputs from GCMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and bias correct and downscale them to a 4‐km resolution. Through leave‐one‐out cross‐validation, we compare various bias correction methods and identify that the empirical quantile mapping approach performs the best regardless of the climate variable. Then, we analyse the projected bias‐corrected outputs for two future periods (Mid‐of‐Century: 2015–2048; End‐of‐Century: 2067–2100) with respect to the 1981–2014 period, under four shared socioeconomic pathway scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). Our results show that Arizona's climate is projected to become overall warmer and wetter, more so towards the end of this century and for higher emission scenarios. Additionally, our findings project an increase in wet bulb temperature and cooling degree days, implying the ongoing warming climate's potential impacts on public health and the economy. These results provide a baseline understanding of climate change impacts in the state and highlight the projected changes in the future in response to the climate scenarios. [ABSTRACT FROM AUTHOR]

While previous studies have largely focused on anthropogenic warming characterized by surface air temperature, little is known about the behaviors of human‐perceived temperature (HPT), which describe the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity and/or wind speed. Here we adopted an optimal fingerprinting method to compare seasonal mean HPTs in China with those from simulations conducted with multiple climate models participating in the Coupled Model Intercomparison Project Phase 6. We found clear anthropogenic signals in the observational records of changes in both summer and winter HPTs over the period 1971–2020. Moreover, the anthropogenic greenhouse gas influence was robustly detected, with clear separation from natural and anthropogenic aerosol forcings. The anthropogenic greenhouse gas forcing plays the dominant role (>90%) of human‐perceived warming. Urbanization effects contribute slightly and moderately to the estimated trends in summer and winter HPTs, respectively, in addition to the effects of external forcing. Plain Language Summary: Human influences have been identified in the observed warming quantified by surface air temperature (SAT), but SAT alone is inadequate as a metric for human thermal comfort. Here we focus on human‐perceived temperature (HPT), which describes the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity, and/or wind speed. We isolate anthropogenic impacts on the observed increase in summer and winter HPTs in China during 1971–2020 by comparing observations with state‐of‐the‐art climate models. Results show that the influence of anthropogenic greenhouse gas is detected, with clear separation from other external forcings such as solar and volcanic activities and anthropogenic aerosols. The human‐induced greenhouse gas increases are also found to explain most (>90%) of the observed human‐perceived warming. Along with the effects of large‐scale anthropogenic forcing, urbanization effects also have a slight to moderate influence on the estimated trends in summer and winter HPTs. Our work is an early attempt to provide quantitative evidence for the physiological impacts of anthropogenic global warming and local urbanization on human beings. Key Points: The warming is quantified by human‐perceived temperature that considers the joint effects of temperature, humidity and/or wind speedHuman influence could be robustly detected in both summer and winter human‐perceived warmingThe observed increase in human‐perceived temperature is mostly attributed to anthropogenic greenhouse gas increases [ABSTRACT FROM AUTHOR]

The unprecedented 2021 June‐July heatwave in Western North America resulted in record‐breaking human‐perceived heat stress across the region, measured by the humidex considering both air temperature and humidity. During extended summer (June‐September), both 95th percentiles of daily maximum humidex (HX95) and air temperature (TX95) have increased over the 1940–2022 period, with even faster intensification in the last two decades (2001–2022). HX95 has increased more than TX95 because of the positive monotonic nonlinear relationship between humidex and air temperature at a given level of relative humidity. The Canadian Earth System Model version 5 (CanESM5) projects a larger increase in human‐perceived heat stress than air temperature across the region under low to high emission scenarios (HX95 increases 4.40–7.04°C and TX95 increases 2.92–4.65°C between 1981–2010 and 2041–2060). Moreover, CanESM5 projects significant increases in the frequency of HX and TX conditions that exceed the levels reached in 2021 under intermediate and high emission scenarios. Plain Language Summary: Using humidex, a measure that combines air temperature and humidity, we assessed the human‐perceived heat stress during the record‐breaking 2021 summer heatwave in Western North America. The 2021 heatwave broke records for both air temperature and humidex, and these two measures have been increasing in the past decades. The heatwave was drier than usual due to high pressure in the atmosphere, clear skies, and more heat warming up the air instead of evaporating water. Humidex will rise more than air temperature under various emission scenarios, making people feel even hotter in the region. The climate model used suggests that humidex values and temperatures that exceed the levels observed in 2021 will happen much more often and be much more intense across the region in the future, with estimates of three to ten times for humidex and two to seven times for air temperature more frequent under intermediate to high emission scenarios by the end of the century. However, the likelihood of extreme humidex in dry conditions, as observed in 2021, will remain low, like historical levels. Key Points: Unprecedented 2021 heatwave in Western North America resulted in record‐breaking human‐perceived heat stress, considering both air temperature and humidityExtreme human‐perceived heat stress increased at a faster rate than extreme air temperature, both showing rapid increases in recent decadesFor events exceeding 2021 level, a larger future increase in extreme human‐perceived heat stress is projected compared to air temperature [ABSTRACT FROM AUTHOR]

Future changes in Sahel precipitation are uncertain because of large differences among projections from various models. In order to explore this uncertainty, we use a storyline approach which seeks to identify alternative plausible evolutions of Sahel precipitation and their driving factors. By analyzing projections from the CMIP6 climate models, we show that changes in North Atlantic and in Euro‐Mediterranean temperatures explain up to 60% of the central Sahel precipitation change uncertainty. We then construct several storylines of Sahel precipitation change based on future plausible changes in North Atlantic and Euro‐Mediterranean temperatures. In one storyline, an amplified warming of both the North Atlantic and the Euro‐Mediterranean areas promotes a northward shift of the West African monsoon, increasing precipitation over the central Sahel, while, in the opposite storyline, a moderate warming in both regions is associated with a small change in precipitation over the central Sahel and a decrease in precipitation over the western Sahel, at the end of the 21st century. These results indicate that Sahel precipitation uncertainty will not be substantially reduced unless the uncertainty in the future warming of the North Atlantic and the Euro‐Mediterranean areas is constrained. Plain Language Summary: Variations in the strength of the West African Monsoon (WAM) circulation have societal impacts on around 80 million people from Senegal to Chad. However, future projections of the WAM precipitation are uncertain for the end of the 21st century because of strong differences from one climate model to another. We show here that sources of uncertainty in Sahel precipitation depend on changes in North Atlantic and Euro‐Mediterranean temperature, relative to changes in global mean surface air temperature. We show that differences in how climate models simulate the future warming over the North Atlantic and Euro‐Mediterranean area explain a large proportion of the uncertainty in precipitation change over the central Sahel. We provide a method that could be helpful at selecting models for impact studies, based on their sensitivity to climate change over the North Atlantic ocean and Mediterranean Sea. Key Points: Future changes in Sahel precipitation are uncertainChanges in North Atlantic and Euro‐Mediterranean temperatures explain up to 60% of the Sahel precipitation change uncertaintyUncertainty in changes in Sahel precipitation is associated with uncertainty in the future northward shift of the Saharan heat low [ABSTRACT FROM AUTHOR]

The present research focuses on temperature change signals over the Carpathian Basin with a special focus on selected lowland and mountainous subregions. High‐resolution (0.11°) EURO‐ and Med‐CORDEX regional climate model (RCM) simulations of near‐surface air temperature are analysed based on raw and bias‐adjusted data. The mini‐ensemble consists of eight RCM simulations driven by five different general circulation models for the period 1976–2099 under the high‐end RCP8.5 scenario. The high‐resolution, homogenized and quality controlled CARPATCLIM was used as a reference dataset. The selected subregions cover eight municipalities located at diverse altitudes: Bratislava, Budapest, Brassov, Debrecen, Hoverla, Novi Sad, Pécs and Poprad. The following climate indices are assessed: summer days, ice days, frost days, tropical nights, the coldest day, the warmest day, the coldest night and the warmest night. In general, for the reference period (1976–2005) bias‐adjusted RCM data showed almost perfect match with observations. Accordingly, no best performing RCM is found for all indices. The ensemble mean of the bias‐adjusted RCM simulations projects an increase (decrease) of 32% and 112% (18% and 25%) in the annual number of summer days and tropical nights (frost days and ice days) for the period 2021–2050. For 2070–2099 we can expect more frequent tropical nights (about five times) with respect to the reference period and the frequency of frost days can be halved. Profound warming manifests in the increase of the warmest temperature of day of up to 2–3°C by the near future and of 5–7°C by the end of the 21st century, which means the absolute maximum temperature can reach 44–47°C for the period 2070–2099. Our results also highlight the need for bias‐adjusted data adapted by different sectors (human health, agriculture, transport, disaster management, heritage conservation) under the national adaptation strategies. [ABSTRACT FROM AUTHOR]

High‐resolution (dx = 25 km) simulations of the regional climate model RegCM4.7 coupled with the Community Land Model version 4.5 (CLM4.5) under low (RCP2.6) and high (RCP8.5) emissions scenarios, which interact with the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (AR5‐IPCC), were carried out over tropical South America (TSA). These simulations were produced through boundary conditions from simulations driven by the general circulation model HadGEM2‐ES. With the goal of verifying the added value (AV) of RegCM4.7, reproducing in an adequate and coherent way the regional aspects of the historical period (1986–2005), as well as evaluating the regional aspects simulated by the model in the scope of the change projections for the far‐future (2080–2099). For this study, the climate in the TSA was characterized based on the variables of precipitation and near‐surface air temperature. For the evaluation of the spatial–temporal representation, frequency and distribution of the regional and global simulation, the high‐resolution observational dataset of the Climate Research Unit version ts4.02 (CRU) was used. Although some differences and biases still persist, RegCM4.7 presents AV in the spatial representation of precipitation and temperature over the northeast region of Brazil and part of the Andes, especially in winter. However, it does not adequately represent precipitation over the Amazon Basin, especially in summer. Results for future projections indicate that the more refined simulation of RegCM4.7 improves the projected change patterns of the coarser resolution simulation of HadGEM2‐ES and even modifies the precipitation signal in some cases. Both models project increase temperature with greater magnitude for RCP8.5. RegCM4.7 presents a much more refined and realistic spatial distribution. HadGEM2‐ES simulates the major aspects of climate enough to consider forcing RegCM4.7 to generate simulations with better performance and more realistic projections. [ABSTRACT FROM AUTHOR]

Four climate parameters (i.e. maximum, mean and minimum air temperature and precipitation amount) from 10 regional climate models, provided by the EURO‐CORDEX initiative, are adjusted using as reference the ROCADA gridded dataset. The adjustment was performed on a daily temporal resolution for the historical period (1971–2005), as well as for climate change scenarios based on two Representative Concentration Pathways (RCP4.5 and RCP8.5). The most accurately method for bias‐correction (BC) was selected following a 2‐fold cross‐validation approach, which was performed on historical data using two methods: Quantile Mapping (QMAP) and Multivariate Bias Correction with N‐dimensional probability (MBCn). The performances of the two methods are very similar when analysing the frequency distribution of each selected variable, whereas the comparison between (1) the intervariables correlation of the adjusted datasets, and (2) the reference dataset revealed much smaller differences for the dataset adjusted with the multivariate method, hence this was used for producing the BC scenario dataset. Based on the MBCn adjusted dataset, a climate change analysis over Romania was tested at the seasonal and annual scale. Overall, for the multimodel ensemble mean, at the country level, a substantial temperature increase is reported for both scenarios and no significant trend is revealed for precipitation amount. The adjusted RCMs are provided without any restrictions via an open‐access repository in netCDF CF‐1.4‐compliant file format. The BC climate models are archived at the 0.1° spatial resolution, in the WGS‐84 coordinate system, at a daily temporal resolution. [ABSTRACT FROM AUTHOR]

Climate change in the Arctic has substantial impacts on human life and ecosystems both within and beyond the Arctic. Our analysis of CMIP6 simulations shows that some climate models project much larger Arctic climate change than other models, including changes in sea ice, ocean mixed layer, air‐sea heat flux, and surface air temperature in wintertime. In particular, dramatic enhancement of Arctic Ocean convection down to a few hundred meters is projected in these models but not in others. Interestingly, these models employ the same ocean model family (NEMO) while the choice of models for the atmosphere and sea ice varies. The magnitude of Arctic climate change is proportional to the strength of the increase in poleward ocean heat transport, which is considerably higher in this group of models. Establishing the plausibility of this group of models with high Arctic climate sensitivity to anthropogenic forcing is imperative given the implied ramifications. Plain Language Summary: Arctic climate is projected to change dramatically in the future based on the latest climate models. However, there is a large discrepancy between these climate models. We find that simulated Arctic climate change tends to be much larger in models using the community ocean model "NEMO" as their ocean component. Future Arctic climate change in winter strongly depends on the strength of the increase in poleward ocean heat transport, which is considerably higher in the NEMO‐family climate models. There are some indications that this group of models can better reproduce the observed winter sea ice decline and mixed layer depth in the Barents Sea in their historical simulations. We suggest that investigating the model physics in the NEMO‐family climate models in comparison with other models may ultimately help to reduce the overall uncertainty in climate change projections. Key Points: Future Arctic climate change in winter strongly depends on the strength of the increase in poleward ocean heat transport (OHT)Arctic climate change in the NEMO‐family climate models is much more pronouncedNEMO‐family climate models have larger increases in poleward OHT [ABSTRACT FROM AUTHOR]

Given the strong sensitivity of sweet cherry trees to air temperature and the foreseeable warming under future climates, it becomes urgent to analyse spatiotemporal variability of growing conditions in main production areas of China. Here, we estimated changes of three climate metrics, that is, winter chill, heat accumulation and the risk of spring frost events, by using 22 global climate models over the historical and future (represented by central years 2050s and 2085s) time stages. Statistically downscaled daily maximum and minimum temperatures at 14 sites under representative concentration pathway (RCP) 4.5 and 8.5 scenarios were used. The results show a general increase of available winter chill for most sites in Bohai and Lanzhou–Lianyungang Railway line agro‐climatic zones, and the increase in chill accumulation could reach up to 15.2% in these regions for RCP4.5 by 2085 s. However, the most dramatic winter chill decrease is projected to occur in the southwest region under the RCP8.5 scenario by 2085 s. Additionally, the increase rate of heat accumulation during the forcing period shows spatially consistency, and the most pronounced increase is found in the RCP8.5 by 2085 s. In the north region, median heat accumulation increases by 17.5%–21.0% in the 2050 s under RCP4.5. Similar increasing range could be found in the southwest station. High frost risk areas are found in the southwestern region for both baseline and future climate scenarios. Across the 14 study sites, Mengzi and Kunming have the highest meteorologically defined risk of spring damaging frost with accumulated 335 growing degree days (GDDs) and 264 GDDs before the last spring frost event, respectively. Overall, this study provides projected characteristics of site‐specific growing conditions for sweet cherry trees in main production regions. The results could be useful for decision‐making such as selection of appropriate species and varieties in adapting to future warming. [ABSTRACT FROM AUTHOR]

Within the statistical climatology literature, inferring the contributions of potential causes with regard to climate change has become a recurrent research theme during this last decade. In particular, disentangling human induced (anthropogenic) forcings from natural causes represents a nontrivial statistical task, especially when the focal point moves away from mean behaviors and goes towards extreme events with high societal impacts. Most studies found in the field of extreme event attributions (EEA) rely on extreme value theory. Under this theoretical umbrella, it is often assumed that, for a given location, temporal changes in extremes can be detected in both location and scale parameters of an extreme value distribution, while its shape parameter remains unchanged over time. This assumption of constant tail shape parameters between a so‐called factual world (all forcings) and a counterfactual one (without anthropogenic forcing) can be challenged due to the fact that important forcing changes could impact large scale atmospheric and oceanic circulation patterns, and consequently, the latter can reshape the full distribution, including its shape parameter that drives extremal behavior. In this article, we study how allowing different tail shape parameters between the factual and counterfactual worlds can affect the analysis of records. In particular, we extend the work of Naveau et al. in which this case was not treated. We also add properties and theoretical inferential results about records in EEA and propose a procedure for model validation. A simulation study of our approach is detailed. Our method is applied to records of yearly maxima of daily maxima of near‐surface air temperature issued from the numerical climate model CNRM‐CM6‐1 of Météo‐France. [ABSTRACT FROM AUTHOR]

This study investigates some of the uncertainties sources associated with the pseudo global warming (PGW) approach which was employed to project future patterns of tropical cyclones (TCs) over the Arabian Sea (AS). First, the climate variables controlling the patterns of tropical cyclones were extracted from reanalysis datasets of ERA5, ERAI, CFSR, and NCEP/NCAR. Then, each dataset was evaluated against long‐term measurements to identify the best‐performing reanalysis dataset. ERA5 showed the best performance for most of the variables. Outputs of 20 CMIP5 global climate models (GCMs) were then evaluated against the ERA5 data resulting in an ensemble of the best performing GCMs. A PGW framework was then used to project the changes in patterns of three significant historical cyclones: Gonu, Phet, and Ashobaa. In doing so, the signals of future climate variables were extracted from the GCMs ensemble to modify the initial and boundary conditions of the WRF model which was previously tuned for reproducing the historical TCs. Different tests were conducted to address the sources of uncertainty in the PGW approach, including the selection of the climate variables contributing to the computation of the signals, the selection of GCMs, and the spatial variation of signals. A considerable sensitivity of the projected track and intensity of TCs to the choice of GCMs was observed, acknowledging the importance of GCMs evaluation before calculating the signals. Moreover, it was found that among all variables, signals of sea surface temperature and air temperature have major effects on the cyclone's track and intensity. Apart from that, when the signals were applied to the domain of the WRF model uniformly, compared to applying spatially varying signals, different tracks and intensities for future TCs were also observed. Overall, the findings of this paper challenge the reliability of the projected changes in TCs patterns obtained from PGW. [ABSTRACT FROM AUTHOR]

This study aims to show how future groundwater recharge (GR) simulations in arid areas respond to uncertainty in climatic parameters—a question, if explored, that bridges a gap in water resources management plans. To this aim, eight regional climate models (RCMs) under two representative concentration pathways (RCP4.5 and RCP8.5) projected four climatic parameters [surface air temperature, precipitation, wind speed, and potential evapotranspiration (PET)] over Qatar during the period of 2071–2100. Using topographic and groundwater data, a physically based water balance model was built to simulate future GR under these 16 scenarios. Results show high uncertainty in climatic parameters. Relative to the reference period (1976–2005), values varied under RCP4.5 (RCP8.5) from +1.8 to +3.4 (+3.8 to +5.6)°C for average temperature, −48% to +15% (−60% to +6%) for annual precipitation, −0.23 to +0.1 (−0.27 to +0.04) m/hour for wind speed, and from −5.7 to +12.8 (+4.3 to +17) mm for annual PET. Uncertainty in climatic parameters caused great uncertainty in future GR estimations. During the late 21st century, GR simulations varied from −67% to +64% with an average value of −20% under RCP4.5, and from −81% to +8% with an average value of −36% under RCP8.5. The greatest uncertainty resulted from the driving model, whereas the choice of emission scenario had a secondary impact. Since GR is a critical component of feeding arid aquifers, the study's findings emphasize the importance of both considering the uncertainty associated with climatic parameters and the regional climatic information chosen. [ABSTRACT FROM AUTHOR]

While the annual mean Arctic Amplification (AA) index varied between two and three during the 1970–2000 period, it reached values exceeding four during the first two decades of the 21st century. The AA did not change in a continuous fashion but rather in two sharp increases around 1986 and 1999. During those steps the mean global surface air temperature trend remained almost constant, while the Arctic trend increased. Although the "best" CMIP6 models reproduce the increasing trend of the AA in 1980s they do not capture the sharply increasing trend of the AA after 1999 including its rapid step‐like increase. We propose that the first sharp AA increase around 1986 is due to external forcing, while the second step close to 1999 is due to internal climate variability, which models cannot reproduce in the observed time. Plain Language Summary: The rate of Arctic warming varied in time, while the rate of global warming was nearly constant over the period 1960–2020. This led to a variable Arctic Amplification (AA) defined as a ratio of the Arctic warming trend to mean global warming trend. In addition, the Arctic experienced sudden changes in the rate of warming with step‐like changes in annual mean Arctic Amplification around the years 1986 and 1999. Climate models do not capture the second observed sharp increase of the AA. We suggest that the first AA increase around 1986 is primarily due to external forcing (increasing concentration of carbon dioxide) while the second sharp increase around 1999 is dominated by internal climate variability which current models cannot capture accurately within the time scale of the physical phenomena. Key Points: Annual mean Arctic Amplification (AA) within the period 1970–2020 changed in steep steps around 1986 and 1999. It reached values over 4.0Even those CMIP6 models best at reproducing the AA did not reproduce the second sudden increase near 1999We conjecture that the first AA sharp increase is due to external forcing, while the second is due to internal climate variability [ABSTRACT FROM AUTHOR]

Past studies based on univariate scaling analyses at the weather time scale documented a negative scaling of extreme precipitation intensity (EPI), which prevents EPI extrapolation from past climate. Here we present a bivariate scaling analysis and show that, contrary to the univariate scaling results, EPI monotonically increases with temperature and shows no negative scaling when controlled for saturation deficit. The observed EPI‐temperature relationship in saturated atmosphere is surprisingly similar among different regions and closely follows the Clausius‐Clapeyron scaling; climate models produce greater regional dependence of the scaling relationship with a wide range of scaling rate. For extratropical regions, the model‐simulated EPI‐temperature relationship under saturation shows a past‐to‐future continuity, which could potentially support extrapolation to a warmer climate. The scaling at saturation bridges the EPI‐temperature relationship between weather and climate time scales and may enable potential prediction of future precipitation extremes via extrapolation from past observations. Plain Language Summary: Predicting changes of extreme precipitation intensity (EPI) and their regional dependency is a crucial but challenging task. Extrapolation based on past observations has been hindered by the apparent decrease of EPI on hot days, a phenomenon often referred to as "negative scaling". Here we propose a bivariate scaling analysis to account for the impact of both temperature and atmospheric saturation deficit (SD), and show that under a constant SD, EPI increases monotonically with temperature and shows no negative scaling, which contradicts the findings from conventional scaling analysis. In saturated atmosphere, the EPI‐temperature relationship based on observations is surprisingly similar among different regions and is close to the thermodynamics‐based Clausius‐Clapeyron scaling; climate models produce a larger disparity among different regions with wide ranging scaling rates. As climate changes, the model simulated EPI‐temperature relationships in saturated atmosphere over extratropical regions show a certain degree of continuity between historical and warmer climates, contrasting the strong discontinuity in conventional analysis. The past‐to‐future continuity of the relationship makes it possible to derive future extreme precipitation through extrapolation from past observations. The scaling relationship in saturated atmosphere bridges the weather and climate time scales, and provides a new emergent constraint for climate model performance. Key Points: Under a constant saturation deficit, extreme precipitation intensity increases monotonically with temperature, showing no negative scalingThe observed precipitation scaling in saturated atmosphere shows little regional dependence and is close to the Clausius‐Clapeyron ratioClimate models produce larger regional dependence, but show some past‐to‐future continuity of the scaling relationship [ABSTRACT FROM AUTHOR]

For future climate projections to be useful they must be actionable at the local level. In this study, we develop daily temperature and precipitation climate scenarios suitable for use in projections of drought, energy use, water use, and crop production. We investigate the magnitude of future changes to air temperature and precipitation in the Midwest United States in response to three future climate change scenarios. Results are used to assess changes to incidence of precipitation extremes and human comfort (using heat index) associated with the anticipated climate changes in the region. We use self‐organizing maps and random forest based techniques to generate daily realizations of temperature and precipitation for 279 weather stations in a region centred on Illinois. We determine that the random forest model performs best for maximum and minimum temperatures, while the self‐organizing map performs best for precipitation. Using nine models from the Coupled Model Inter‐Comparison Project Phase 5, downscaled daily temperature and precipitation values are generated for low, moderate, and high greenhouse gas emissions scenarios for historical and future periods. Based on recent trends, we focus our results on the high emissions scenario, and show an average increase of 4.3°C in maximum daily air temperature across the region for the 2071–2100 period. Precipitation decreases by up to 15% in the southern half of the study region, with a similar percentage increase in the northern half of the region. The regional environmental changes result in an increase of 5.8° in average summer heat index, and increase of 48% in the number of days likely to produce extreme heat, and a decrease in the average value of the standardized precipitation and evapotranspiration index of 1.9 (indicating increased drought) across the region by 2100. [ABSTRACT FROM AUTHOR]

Global climate change is a change in the planetary energy (E) balance. It is usually expressed as a change in near‐surface air temperature (Ta), but changes in energy content due to Ta (ET) represent only part of the near‐surface energy balance, which also includes E changes due to specific humidity (ESH). We analyzed MERRA‐2 and ERA5 reanalysis data and 15 CMIP6 Atmospheric Model Intercomparison Project (AMIP) models from 1980 to 2014. Some 44%, 39%, and 50% of MERRA‐2 pixels showed significant increases in ET, ESH, and E, respectively. The average increase in ET (ESH) was 16.9 (13.6) J kg−1 year−1, but AMIP models estimated larger average ET (21.6 J kg−1 year−1) and ESH (16.7 J kg−1 year−1). Global Ta would have increased at nearly double the observed rate if energy was not partitioned into latent heat. Results demonstrate the critical role that specific humidity plays in recent climate changes. Plain Language Summary: Greenhouse gases trap longwave radiation, making it more difficult for energy to leave Earth. This increases air temperatures, but there is much more to global climate change than air temperature alone. Most of the excess energy caused by the increase in greenhouse gases has entered the ocean, because it takes a lot of energy to heat water. The water vapor content of the atmosphere near the surface has also increased, and it takes energy to heat and evaporate this water. We find that the energy needed to evaporate the extra water that has entered the atmosphere has helped buffer increases in global air temperatures, which would have increased by nearly double if this energy was used to increase air temperatures alone. Global climate models can simulate many aspects of these energy changes, but struggle in regions where regional climate is complex. Many of the largest increases in atmospheric energy have occurred in populous regions of the globe while the greatest temperature increases are near the poles. It is important to understand that global change is a change in the planetary energy balance, and that near surface air temperature alone is only a small (but important) part of this energy change. Key Points: The temperature and specific humidity of the near‐surface atmosphere are increasingBoth imply an increase in atmospheric energy content, and climate models often struggle to simulate regional changesThe rate of air temperature increase would have more than doubled without specific humidity change, all else being equal [ABSTRACT FROM AUTHOR]

Large artificial lakes and reservoirs affect the meteorological regime of the shore area and the local climate takes on a number of new features that were previously absent. This work focuses on the weather impact of the Alqueva reservoir, the largest artificial lake in Western Europe. An extensive set of numerical simulations using Meso‐NH mesoscale atmospheric model coupled with FLake (Freshwater Lake) scheme was carried out. The simulations covered a 12‐month period that was chosen to compose a so‐called Typical Meteorological Year. This artificial time period is meant to represent the typical meteorological conditions in the region and the model results are used to assess the changes in the local climate. To evaluate the raw impact of the reservoir, two different scenarios of simulations were compared: (A) with the reservoir as it exists nowadays and (B) without the reservoir using the older surface dataset. The results show decrease of air temperature during daytime (10–9°C) and nighttime increase (up to 10°C). In nearest towns, daily maximum temperature decreased and daily minimum temperature increased, which refers to milder weather conditions. Alqueva mainly showed suppression in fog formation in the nearby area. Local breeze regime was studied and monthly lake/land breezes were described. [ABSTRACT FROM AUTHOR]

Climate modeling studies and observations do not fully agree on the implications of anthropogenic warming for evapotranspiration (ET), a major component of the water cycle and driver of irrigation water demand. Here, we use California as a testbed to assess the ET impacts of changing atmospheric conditions induced by climate change on irrigated systems. Our analysis of irrigated agricultural and urban regions shows that warmer atmospheric temperatures have minimal implications for ET rates and irrigation water demands—about one percent change per degree Celsius warming (∼1% °C−1). By explicitly modeling irrigation, we control for the confounding effect of climate‐driven soil moisture changes and directly estimate water demand implications. Our attribution analysis of the drivers of ET response to global anthropogenic warming shows that as the atmospheric temperature and vapor pressure deficit depart from the ideal conditions for transpiration, regulation of stomata resistance by stressed vegetation almost completely offsets the expected increase in ET rates that would otherwise result from abiotic processes alone. We further show that anthropogenic warming of the atmosphere has minimal implications for mean relative humidity (<1.7% °C−1) and the surface available energy (<0.2% °C−1), which are critical drivers of ET. This study corroborates the growing evidence that plant physiological changes moderate the degree to which changes in potential ET are realized as actual ET. Plain Language Summary: Climate modeling studies and observations do not fully agree on the implications of climate change‐induced warming of the atmosphere for evapotranspiration (ET), a primary driver of irrigation water demand, despite its scientific and societal importance. Here, we use California as a testbed where we focus on vast irrigated urban and agricultural areas to assess the impacts of the warming climate on ET and irrigation water demand. Our analysis shows that warmer atmospheric temperatures have minimal implications for ET rates and irrigation water demand. We show that as the atmospheric temperature and humidity depart from the ideal conditions for transpiration, regulation of stomata resistance by stressed vegetation limits the increases in ET rates that would otherwise result from the increasing demand for moisture in the warmer atmosphere. Key Points: Increasing atmospheric temperature and vapor pressure deficit have minimal implications for evapotranspiration (ET) and irrigation water demandRegulation of stomata resistance by stressed vegetation offsets the expected increase in ET rates that would otherwise result from abiotic processes aloneAnthropogenic warming of the atmosphere has minimal implications for mean relative humidity and the surface available energy, which are critical drivers of ET [ABSTRACT FROM AUTHOR]

Amid reports from western US wildland fire managers that, compared to when many started their careers, fires are burning longer throughout the day before reducing in intensity overnight, we examined decadal changes in nighttime vapor pressure deficits over the western United States with a focus on the summer fire season. We calculated changes using a recently updated observation‐assimilating reanalysis (ERA5) available at hourly resolution over the 1980–2019 period. Analysis identifies the proximate cause (atmospheric temperature vs. moisture content) of the observed changes and the extent to which they have been captured in climate model simulations. Increases in nighttime vapor pressure deficits of >50% in 40 years are evident over foothills of mountains ranges adjacent to arid plateaus. The largest observed increases greatly exceed the forced climate‐model response. Correlation analysis reveals a broad link between the variability of western US summer‐nighttime vapor pressure deficit and the Pacific Decadal Oscillation. Plain Language Summary: Western US wildland fire managers have reported that fires are burning longer into the night and increasing in intensity earlier in the morning compared to when many started their careers. Increasing nighttime vapor pressure deficit—a measure of the drying power of air—is widely suspected to be responsible for these perceived changes in fire behavior. It is also suspected that human‐caused increases in nighttime temperatures are responsible. We used a recently released observation‐based data set to quantify the extent to which nighttime vapor pressure deficits have changed over the last 40 years and determine the proximate cause for the observed changes (particularly whether temperature effects on their own can explain them). We also explored how well the observed changes are captured in climate model simulations. Results highlight large increases in nighttime vapor pressure deficit over the foothills of mountain ranges next to arid plateaus, where the observation‐based increases greatly exceed the climate model projections, and both temperature and humidity play a role in driving the observed changes. Key Points: Nighttime vapor pressure deficit increased >50% in 40 years over the foothills of the Sierra Nevada and Rocky MountainsSaturation vapor pressure increases caused by rising temperatures and decreases in actual vapor pressure account for these changesThe magnitudes of the changes in the observation‐based records greatly exceed those projected by climate forecast models [ABSTRACT FROM AUTHOR]

Climate change will alter stream habitats through precipitation and air temperature changes and potentially threaten species that rely on contemporary streamflow and stream temperature regimes. Habitat projections are therefore critical to inform management decisions. Past and ongoing research has improved streamflow and temperature modelling in ungauged regions, but no studies merge these advancements with climate modelling for regional streamflow and stream temperatures predictions that describe stream habitat change. Here, we predict change in streamflow and stream temperature at the reach scale using projections from downscaled global climate models (GCMs) and the 'business as usual' carbon emission scenario. We focus on unaltered streams in six southern California watersheds using data from baseline (1982–2014) and projected end‐of‐century (2082–2100). Stream temperature is projected to increase regionally, with high‐elevation stream reaches increasing most rapidly. There is less consistency in the streamflow projections, but a spatial and temporal homogenization of stream flow characteristics was predicted, that is, flows become more similar across the region with less inter‐annual variation. Additionally, there is a regional trend towards larger high flow magnitudes and more storm events. Despite the increased frequency and magnitude of storm events, high‐elevation streams are predicted to become drier for a greater portion of the year. Conversely, low‐elevation streams are predicted to have longer hydroperiods. Mapping future streamflow and stream temperatures at the reach scale can direct conservation efforts to streams that remain suitable, restoration to areas that decrease in suitability for target species, and support water policies that consider future stream condition. [ABSTRACT FROM AUTHOR]

Global warming is leading to increased heat stress in many regions around the world. An extensive number of heat stress indicators (HSIs) has been developed to measure the associated impacts on human health. Here we calculate eight HSIs for global climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). We compare their future trends as function of global mean temperature, with particular focus on highly populated regions. All analyzed HSIs increase significantly (p < 0.01) in all considered regions. Moreover, the different HSIs reveal a substantial spread ranging from trends close to the rate of global mean temperature up to an amplification of more than a factor of two. Trends change considerably when normalizing the HSIs by accounting for the different scales on which they are defined, but the large spread and strong trends remain. Consistently, exceedances of impact‐relevant thresholds are strongly increasing globally, including in several densely populated regions, but also show substantial spread across the selected HSIs. The indicators with the highest exceedance rates vary for different threshold levels, suggesting that the large indicator spread is associated both to differences in trend magnitude and the definition of threshold levels. These results highlight the importance of choosing indicators and thresholds that are appropriate for the respective impact under consideration. Additionally, further validation of HSIs regarding their capability to quantify heat impacts on human health on regional‐to‐global scales would be of great value for assessing global impacts of future heat stress more reliably. Plain Language Summary: Heat stress caused by high levels of air temperature and humidity is rising globally due to climate change. Various indicators for heat stress have been developed to quantify different facets of how heat impacts people. We use data from climate models to calculate the future evolution of eight heat stress indicators for highly populated regions of the world. The trends of the different indicators vary substantially, with some indicators showing large increases while others only increase modestly. For estimating the severity of heat stress, we calculate how often each indicator exceeds threshold values that indicate different heat stress severity. Many thresholds will be exceeded much more often with rising temperatures. The increases are particularly large for some indicators while others only show small increases. Moreover, the indicators with the strongest trends are often not the ones that show the highest increase in threshold exceedances. For quantifying the impacts of heat stress caused by climate change it is thus important to choose indicators that are appropriate for the respective application. While several indicators were tested on small scales (e.g., in cities or single countries), for global heat stress assessments it is necessary to have more validation studies on regional‐to‐global scales. Key Points: All heat stress indicators increase statistically significantly with global mean temperature but trends reveal a substantial spreadExceedances of impact‐relevant thresholds are strongly increasing globally including in several densely populated regionsFor assessing heat‐related health impacts only indicators and thresholds should be chosen that were validated for the respective application [ABSTRACT FROM AUTHOR]

Atmospheric moisture holding capacity increases with temperature by about 7% per °C according to the Clausius‐Clapeyron relationship. Thermodynamically then, precipitation intensity should exponentially intensify and thus worsen flood conditions as the climate warms. However, regional and global analyses often report a nonmonotonic (hook) scaling of precipitation and runoff, in which extremes strengthen with rising temperature up to a maximum or peak point (Tpp) and decline thereafter. The underlying cause of this hook structure is not yet well‐understood, and whether it may shift and/or regulate storm runoff extremes under anthropogenic climate warming remains unknown. Here, we examine temperature scaling of precipitation and storm runoff extremes under different climate conditions using observations and large ensemble hydroclimatic simulations over mainland China. In situ observations suggest a spatially homogeneous, negative response of relative humidity to warming climates over 34.6% of the land area, and the remaining hook‐dominated regions usually show a colder Tpp than that of precipitation or storm runoff extremes. The precipitation and streamflow series over mainland China's catchments throughout the 21st century are projected by a model cascade chain under a high‐end emission scenario (RCP 8.5), which involves 31 CMIP5 climate models, 11 CMIP6 climate members, a daily bias correction method, and four lumped conceptual hydrological models. The CMIP5 ensemble projects that the hook structures shift toward warmer temperature bins, resulting in 10%–30% increases in storm runoff extremes over mainland China, while the CMIP6 ensemble projects more severe flood conditions in future warming climates. Plain Language Summary: China is threatened by river floods and has frequently suffered from significant ecosystem damage and economic loss under a changing environment. Storm runoff extremes, an important indicator of flooding regime, have received increasing attention, especially regarding their response to a warming climate. Previous studies have observed a nonmonotonic relationship (called a hook structure) between storm runoff extremes and atmospheric temperatures, in which the extremes strengthen with warming up to a peak point and decline thereafter in a hotter environment. Here, we investigate the ability of such behavior to constrain the intensification of future storm runoff extremes, as well as the underlying mechanisms. We find that the hook structures are not fixed under climate warming but peak at warmer temperatures and higher storm runoff rates. As a result, future flooding risks in mainland China will not be constrained by the hook structures but progressively increase by roughly 10%–30%. Key Points: The temperature scaling of precipitation, storm runoff, and relative humidity are characterized over mainland China's catchmentsPrecipitation and storm runoff usually exhibit a hook structure, with a warmer peak point temperature than that of relative humidityThe hook structures are not fixed under climate change but shift toward warmer temperatures and higher extreme rainfall and runoff rates [ABSTRACT FROM AUTHOR]

In this study, an ensemble of statistically downscaled 14 multi‐global climate models for RCP4.5 and RCP8.5 emission scenarios was employed to implement a comprehensive assessment of climate change impacts over Pakistan in order to identify the future hotspots cities in terms of changes in temperature and precipitation. The analyses focused on the minimum, maximum and average temperature and precipitation in three time‐slices: 2006–2035, 2041–2070, and 2071–2,100. Average temperature is projected to increase by 2.6°C under RCP4.5 while 5.1°C under RCP8.5 by the end of this century with the north side of Pakistan (mainly over North Pakistan—NP, Monsoon Region—MR and Khyber Pakhtunkhwa—KP) presenting the highest changes in the temperatures. Wetter conditions are expected in the future over Pakistan, mainly over the MR. In general, air temperature and precipitation showed linear positive correlation over Pakistan in both RCP scenarios. Hotspot cities where extreme climate, that is, the hottest, dryer and wetter, exists were also identified. Hyderabad will likely become the hottest city of Pakistan by end century with the highest average temperature reaching 29.9°C under RCP4.5 and 32.0°C under RCP8.5 followed by Jacobabad, Bahawalnagar, and Bahawalpur. Most of the hottest cities are detected in areas on the southern side of Pakistan. On the other hand, the wettest cities, Murree, Balakot and Muzaffarabad, are located in the MR. Dry conditions are likely to be prevalent in Dalbandin followed by Khanpur and Jacobabad under both RCPs. The uncertainties of the projections were also evaluated. For precipitation, for example, there are a large number of outliers indicating the high variability/uncertainties of the projections. These uncertainties are clearer when the probability density functions are analysed for individual sub‐domains in Pakistan. [ABSTRACT FROM AUTHOR]

Several far‐infrared (FIR) satellite missions are planned for the next decade, with a special interest for the Arctic region. A theoretical study is performed to help with the design of an FIR radiometer, whose configuration in terms of channels number and frequencies is optimized based on information content analysis. The problem is cast in a context of vertical column experiments (1D) to determine the optimal configuration of a FIR radiometer to study the Arctic polar night. If only observations of the FIR radiometer were assimilated, the results show that for humidity, 90% of the total information content is obtained with four bands, whereas for temperature, 10 bands are needed. When the FIR measurements are assimilated on top of those from the advanced infrared sounder (AIRS), the former bring in additional information between the surface and 850 hPa and from 550 to 250 hPa for humidity. Moreover, between 400 and 200 hPa, the FIR radiometer is better than AIRS at reducing the analysis error variance for humidity. This indicates the potential of FIR observations for improving water vapor analysis in the Arctic. Key Points: FIR channels add information for UTLS water vapor compared to standard MIR channelsIC is used to optimize the channels frequencies and widths of a FIR radiometerA high DFS is reached with only a few channels of an optimized FIR radiometer [ABSTRACT FROM AUTHOR]

The Svalbard Archipelago has undergone rapid warming in the recent decades leading to warmer and wetter winter conditions. This study relates the present (2013–2018) 2 m temperature, precipitation, and rain‐on‐snow (ROS) climate in Svalbard to different atmospheric circulation (AC) types utilizing the high‐resolution numerical weather prediction model Application of Research to Operations at Mesoscale (AROME)‐Arctic. We find that the 2 m median temperatures vary most across AC types in winter and spring and in summer they vary the least. In all seasons the 10th percentile 2 m temperatures are above 0°C with southwesterly AC types over Svalbard. In comparison, the relationship between AC type and precipitation varies more spatially, with most accumulated precipitation and highest median precipitation intensities with onshore flow over open water. Our results suggest that sea ice explains a large part of the local variability in both 2 m temperature and precipitation. In the studied period ROS is a frequent phenomenon up to 150 m above sea level (ASL) on land, with most events in the southwestern parts of the archipelago (57 cases during five winter seasons). ROS events in winter occur predominantly with AC types from the southerly sector or during a low‐pressure center/trough passage. The southwesterly cyclonic AC type, with a low‐pressure center west of Svalbard, is the most frequent AC type for ROS events. In addition to being the most frequent, the southwesterly AC has the largest spatial coverage of ROS. Plain Language Summary: The Svalbard Archipelago has undergone rapid warming in the recent decades leading to warmer and wetter winter conditions. This study relates the present (2013–2018) 2 m temperature, precipitation, and rain‐on‐snow (ROS) climate to different atmospheric circulation (AC) types utilizing the high‐resolution numerical weather prediction model Application of Research to Operations at Mesoscale (AROME)‐Arctic. We found that winter and spring 2 m median temperatures vary most across AC types and in summer they vary the least. Our results suggest that sea ice explains a large part of the local variability in both 2 m temperature and precipitation. In the studied period ROS is a frequent phenomenon up to 150 m ASL on land, with most events in the southwestern parts of the archipelago (57 cases during five winter seasons). The majority of these events occur in southwesterly cyclonic AC type, with a low‐pressure center west of Svalbard. In addition to being the most frequent, the southwesterly AC has the largest spatial coverage of ROS. Key Points: Winter and spring temperature and precipitation conditions in Svalbard are most sensitive to the synoptic flow directionAt the northern and eastern parts of Svalbard the precipitation intensity appears to depend on upstream sea ice conditionsRain‐on‐snow events in Svalbard have the largest spatial coverage under southwesterly synoptic flow [ABSTRACT FROM AUTHOR]

Subseasonal climate prediction has emerged as a top forecast priority but remains a great challenge. Subseasonal extreme prediction is even more difficult than predicting the time‐mean variability. Here we show that the wintertime cold extremes, measured by the frequency of extreme cold days (ECDs), are skillfully predicted by the European Centre for Medium‐Range Weather Forecasts (ECMWF) model 2–4 weeks in advance over a large fraction of the Northern Hemisphere land region. The physical basis for such skill in predicting ECDs is primarily rooted in predicting a small subset of leading empirical orthogonal function (EOF) modes of ECDs identified from observations, including two modes in Eurasia (North Atlantic Oscillation and Eurasia Meridional Dipole mode) and three modes in North America (North Pacific Oscillation, Pacific‐North America teleconnection mode, and the North America Zonal Dipole mode). It is of interest to note that these two modes in Eurasia are more predictable than the three leading modes in North America mainly due to their longer persistence. The source of predictability for the leading EOF modes mainly originates from atmospheric internal modes and the land‐atmosphere coupling. All these modes are strongly coupled to dynamically coherent planetary‐scale atmospheric circulations, which not only amplify but also prolong the surface air temperature anomaly, serving as a source of predictability at subseasonal timescales. The Eurasian Meridional Dipole mode is also tied to the lower‐boundary snow anomaly, and the snow‐atmosphere coupling helps sustain this mode and provides a source of predictability. Key Points: The wintertime land extreme cold days can be largely predicted by the ECMWF model 2–4 weeks in advanceThe physical basis in predicting ECDs is mainly rooted in predicting a small subset of leading EOF modes of ECDs identified from observationThe predictability source for the leading EOF modes originates from atmospheric internal modes and the land‐atmosphere coupling [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]

To counteract global warming, a geoengineering approach that aims at intervening in the Arctic ice‐albedo feedback has been proposed. A large number of wind‐driven pumps shall spread seawater on the surface in winter to enhance ice growth, allowing more ice to survive the summer melt. We test this idea with a coupled climate model by modifying the surface exchange processes such that the physical effect of the pumps is simulated. Based on experiments with RCP 8.5 scenario forcing, we find that it is possible to keep the late‐summer sea ice cover at the current extent for the next ∼60 years. The increased ice extent is accompanied by significant Arctic late‐summer cooling by ∼1.3 K on average north of the polar circle (2021–2060). However, this cooling is not conveyed to lower latitudes. Moreover, the Arctic experiences substantial winter warming in regions with active pumps. The global annual‐mean near‐surface air temperature is reduced by only 0.02 K (2021–2060). Our results cast doubt on the potential of sea ice targeted geoengineering to mitigate climate change. Key Points: Sea ice targeted geoengineering allows to keep the late‐summer sea ice cover at the current extent for the next ∼60 yearsThe Arctic summer cooling is not conveyed to lower latitudes and comes at the price of Arctic winter warmingOur results cast doubt on the potential of Arctic Ice Management to mitigate climate change [ABSTRACT FROM AUTHOR]

Hydrologic intensity is often quantified using precipitation without directly incorporating atmospheric water demand. We develop a hydrologic intensity index called the surplus deficit intensity (SDI) index that accounts for variation in supply and demand. SDI is the standardized sum of standardized surplus intensity (mean of daily surplus when supply > demand) and deficit time (mean of consecutive days when demand > supply). Using an observational ensemble of global daily precipitation and atmospheric water demand during 1979–2017, we document widespread hydrologic intensification (SDI; +0.11 z‐score per decade) driven primarily by increased surplus intensity. Using a climate model ensemble of the United States, hydrologic intensification is projected for the mid‐21st century (+0.86 in z‐score compared to 1971–2000), producing greater apparent intensification when compared to an index that does not explicitly incorporate demand. While incorporating demand had a minor effect on observed hydrologic intensification, it doubles hydrological intensification for the mid‐21st century. Plain Language Summary: Increasing air temperatures have resulted in an intensification, or acceleration, of the terrestrial hydrologic cycle, which is defined as an increase of the water fluxes (precipitation and evapotranspiration) between the surface and the atmosphere. Efforts to quantify hydrologic intensification have traditionally only considered "supply" variables such as precipitation rates without considering "demand" that varies over space and time. For this work we develop a method to quantify hydrologic intensification using supply and demand. This approach shows widespread hydrologic intensification from 1979–2017 across much of the global land surface, which is expected to continue into the future. We additionally compare this new method to a previously developed method that uses only supply without explicitly incorporating demand. Incorporating demand results in a notable hydrologic intensification compared to methods that only use supply or a simplified representation of supply and demand. This work suggests that demand needs to be considered when quantifying hydrologic intensification. Key Points: We develop a hydrologic intensity metric that incorporates daily supply and demand using precipitation and reference evapotranspirationWidespread hydrologic intensification occurred over the past four decades and will continue into the futureAdding demand couples intensity metrics to ecohydrologic conditions and produces greater increases than metrics with static demand [ABSTRACT FROM AUTHOR]

Radiative kernels describe the differential response of radiative fluxes to small perturbations in state variables and are widely used to quantify radiative feedbacks on the climate system. Radiative kernels have traditionally been generated using simulated data from a global climate model, typically sourced from the model's base climate. Consequently, these radiative kernels are subject to model bias from the climatological fields used to produce them. Here, we introduce the first observation‐based temperature, water vapor, and surface albedo radiative kernels, developed from CloudSat's fluxes and heating rates data set, 2B‐FLXHR‐LIDAR, which is supplemented with cloud information from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). We compare the radiative kernels to a previously published set generated from the Geophysical Fluid Dynamics Laboratory (GFDL) model and find general agreement in magnitude and structure. However, several key differences illustrate the sensitivity of radiative kernels to the distribution of clouds. The radiative kernels are used to quantify top‐of‐atmosphere and surface cloud feedbacks in an ensemble of global climate models from the Climate Model Intercomparison Project Phase 5, showing that biases in the GFDL low clouds likely cause the GFDL kernel to underestimate longwave surface cloud feedback. Since the CloudSat kernels are free of model bias in the base state, they will be ideal for future analysis of radiative feedbacks and forcing in both models and observations and for evaluating biases in model‐derived radiative kernels. Key Points: Observation‐based radiative kernels are introduced, providing a method for quantifying feedbacks with reduced model biasAccounting for the cloud masking of noncloud feedbacks is critical for interpreting the sign of longwave cloud feedbackThe distribution of clouds in the polar region contributes to the seasonality of shortwave cloud feedback [ABSTRACT FROM AUTHOR]

ABSTRACT: Despite an increasing body of evidence from observed data that climate change is having a significant impact on different types of biogeophysical systems in the Midwest and Great Lakes region, there still remain critical questions of how quickly and how much climate will be altered over this region in the future. For this evaluation, we make use of 31 global climate model (GCM) projections from the Coupled Model Intercomparison Project, Phase 5 (CMIP5). Based on changes in temperature (T) and precipitation (P) over the Midwest, we selected ten GCM scenarios which (1) simulate historical climate well and (2) successfully capture the range of future climate from the entire CMIP5 ensemble. We then downscaled T and P projections to 1/16° gridded data sets for two different emission scenarios (RCP4.5 and RCP8.5) for three 30‐year future periods using the Hybrid Delta (HD) statistical downscaling approach which was proven to be applicable for daily‐scale application by a validation work using historical data. T is projected to increase across all seasons, with ensemble mean changes up to 6.5 °C by 2100 for the RCP8.5 scenarios. P increases up to 30% in spring and winter with decreasing snowfall to precipitation ratio, while summer P decreases moderately (−15%) by the 2080s. Changes in daily extreme events show similar seasonal patterns including increasing daily extreme P events in winter and decreasing P in summer. Growing season P may actually increase, however, despite projected P reductions in the warmest summer months. Regional warming results in decreased heating degree days (−1639 °C days, −32%) and increasing cooling degree days (+318 °C days, +957%) by 2080s, with overall net reductions in energy demand. [ABSTRACT FROM AUTHOR]

ABSTRACT Developing high-quality long-term data sets at uniform space-time resolution is essential for improved climate studies. This article processes the outputs from two global and regional climate models, the Community Climate System Model ( CCSM3) and the Regional Climate Model driven by the Hadley Centre Coupled Model ( RegCM3). The results are bias-corrected time series of atmospheric variables corresponding to Intergovernmental Panel on Climate Change ( IPCC's) historical ( 20C3M) and future ( A2) climate scenarios over the Amazon Basin. We use a series of simple but effective interpolation approaches to produce hourly climate data sets at 1° by 1° grid cells. A quantile-based mapping approach is used to reduce the biases of temperature and precipitation in CCSM3 and RegCM3. Adjustments are also made on specific humidity and downwelling longwave radiation to avoid inconsistency between those variables and bias-corrected temperature values. We also interpolated an already bias-corrected Parallel Climate Model data set ( PCM1) from 3-hourly to the hourly resolution. The final climate data sets can be used as forcing of ecosystem and hydrologic models to study climate changes and impact assessments over the Amazon Basin. [ABSTRACT FROM AUTHOR]

ABSTRACT The impact of global climate change on urban relative to rural summertime air temperatures is investigated for nine major cities in Germany, under the Representative Concentration Pathway trajectory 8.5. The analysis is based on simulations of representative summers with a regional climate model ( RCM) in conjunction with a multilayer urban parametrization when driven by three global circulation models ( GCMs). The GCMs are selected because they project different warming rates for Germany. Specifically, each GCM provides boundary conditions for the RCM for two past and two future summers that resemble average conditions for Germany with respect to 2-m air temperature statistics during a historical (1976-2005) and a future (2031-2060) period. Thus, one emphasis of our study is on the effects of the different driving GCMs on urban-rural air temperature differences (Δ Tu−r). The simulations show climate change signals ( CCSs) in summer-mean Δ Tu−r of the city ensemble of up to 0.15 K. Across all driving GCMs, the largest changes are projected for the city of Berlin. Characteristics of the diurnal courses of Δ Tu−r, the magnitude of the CCS and its physical reasons are GCM specific. Specifically, CCSs are caused either by positive or negative changes in the Bowen ratio of the rural boundary with little change in the urban surface fluxes, or vice versa. The study emphasizes the importance of the driving GCM in RCM simulations when investigating urban effects on air temperature under global climate change. [ABSTRACT FROM AUTHOR]

ABSTRACT The Koshi river basin is a sub-basin of the Ganges shared among China, Nepal, and India. The river system has a high potential for investment in hydropower development and for irrigation in downstream areas. The upper part of the basin contains a substantial reserve of freshwater in the form of snow and glaciers. Climate variability, climate change, and climate extremes might impact on these reserves, and in turn impact on systems that support livelihoods, such as agriculture, biodiversity and related ecosystem services. Climatological variability and trends over the Koshi river basin were studied using RClimDex. Daily temperature data (20 stations) and precipitation data (50 stations) from 1975 to 2010 were used in the analysis. The results show that the frequency and intensity of weather extremes are increasing. The daily maximum temperature ( TXx) increased by 0.1 °C decade−1 on average between 1975 and 2010 and the minimum ( TNn) by 0.3 °C decade−1. The number of warm nights increased at all stations. Most of the extreme temperature indices showed a consistently different pattern in the mountains than in the Indo-Gangetic plains, although not all results were statistically significant. The warm days ( TX90p), warm nights ( TN90p), warm spell duration ( WSDI), and diurnal temperature range ( DTR) increased at most of the mountain stations; whereas monthly maximum and minimum values of daily maximum temperature, TX90p, cool nights ( TN10p), WSDI, cold spell duration indicator ( CSDI), DTR decreased at the stations in the Indo-Gangetic plains, while the number of cold days increased. There was an increase in total annual rainfall and rainfall intensity, although no clear long-term linear trend, whereas the number of consecutive dry days increased at almost all stations. The results indicate that the risk of extreme climate events over the basin is increasing, which will increase people's vulnerability and has strong policy implications. [ABSTRACT FROM AUTHOR]

ABSTRACT We have analyzed series of air, near-surface and shallow ground temperatures under four land cover types, namely bare clayey soil, sand, short-cut grass and asphalt; the samples were collected between 2002 and 2013 and monitored at the Geothermal Climate Change Observatory Sporilov, Prague (50°02.43′N, 14°28.54′E, 226 m a.s.l.). A comparison of all of the obtained temperature series revealed a strong dependence of the subsurface thermal regime on the respective surface cover material. The ground 'skin' temperature was generally warmer than the surface air temperature over all monitored surfaces; however, the temperatures over different land cover types differed significantly. Asphalt exhibited the highest temperatures, and temperatures below the grassy surface were the lowest. Special attention was paid to assessing the value of the 'temperature offset', the instant value of which sometimes varied dramatically, on both daily and annual scales, by up to 30+ K; however, on a long-time scale, the temperature offset was generally constant and reflected the surface material. The characteristic 2003-2013 mean values for the individual covers are as follows: asphalt 4.1 K, sand 1.6 K, clay 1.4 K and grass 0.2 K. All four surface covers revealed typical daily and inter-annual cycles, which were monitored and are discussed in detail. Incident solar radiation was the primary variable for determining the amount and temporal changes of the temperature offset values. A linear relationship between air-ground temperature differences and incident solar radiation was detected. The mean slope of the linear regression between both variables is clearly surface cover dependent. The greatest value, 3.3 K per 100 W m−2, was found for asphalt cover; rates of 1.0-1.2 apply to bare soil and sand cover, and a negative slope of −0.44 K per 100 W m−2 represents grass cover. [ABSTRACT FROM AUTHOR]

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ABSTRACT The ENSEMBLES regional climate model ( RCM) ensemble is analysed with respect to the elevation-dependency of 21st century near-surface climate change over Europe. Fifteen experiments carried out by 11 different RCMs, driven by 6 different global climate models ( GCMs) are considered, all of them assuming the SRES A1B emission scenario. Model evaluation for the historical period yields an approximate reproduction of observed temperature-elevation and precipitation-elevation profiles, but some distortion due to high-elevation cold and wet biases. Climate change signals until the end of the 21st century show characteristic elevation dependencies over many parts of Europe and in many seasons. Except for northern Europe maximum warming typically occurs at medium to high elevations, leading to a decrease of near-surface temperature lapse rates. The model agreement on the vertical temperature change profile is high; it does not depend on the driving GCM although the latter strongly determines the overall warming magnitude. Warming anomalies can often be related to changes of surface snow cover, and a contribution of the snow-albedo feedback to the anomalous medium- and high-elevation warming is likely. Also climate change signals of seasonal precipitation sums can considerably depend on elevation, but peculiarities highly depend on the region and season considered. Moreover, the vertical precipitation change profile appears to be less related to elevation itself but is strongly influenced by horizontal change patterns within the individual analysis domains and by the relative location of grid cells with respect to major orographic obstacles. Medium- to high-elevation anomalies of large-scale climate change signals as identified in this work represent an added value of dynamical downscaling as these elevations are typically not represented by the driving GCMs. [ABSTRACT FROM AUTHOR]

Other processes that drive heatwaves include sea surface temperature patterns and soil moisture feedback. For the UK, the Met Office defines a heatwave over a location as when it records maximum temperatures greater than or equal to a temperature threshold for at least three consecutive days. [Extracted from the article]

The dependency of the boundary-layer cloud regime on the free tropospheric temperature and humidity is examined. Equilibrium state solutions obtained with the single-column model version of the climate model EC-EARTH are analysed in a phase space defined by the lower tropospheric stability (LTS) and a similar measure for humidity. The set-up comprises two experiments: one with large-scale subsidence which is constant in time and a second one with additional stochastic noise added to the subsidence. The dependency of the boundary-layer state on the free tropospheric conditions is qualitatively consistent between the two experiments. Well-mixed stratocumulus-topped boundary layers are found for high LTS and moist free tropospheric conditions. Cooler and dryer free tropospheric conditions favour the presence of shallow cumulus clouds. Subsequently the response to a sea surface warming of 2 K and a free atmospheric perturbation conserving both the LTS and the relative humidity is assessed. The model predicts an overall positive low-cloud feedback for both the constant subsidence experiment and the experiment with the additional stochastic noise. [ABSTRACT FROM AUTHOR]

This study investigates whether a regional climate model ( RCM) driven by a global general circulation model ( GCM) in a nesting approach with observed atmospheric CO2 concentrations shows predictability for temperature and precipitation trends during 1961-1990 in the Mediterranean area, a region strongly influenced by large-scale circulation. Resulting discrepancies between model and observations raise the question whether the model predictability increases after removing impacts of mid-latitude circulation variability. For temperature and precipitation trends we use the RCM REMO and the observational dataset E- OBS, and for atmospheric circulation the driving coupled GCM ECHAM5/ MPI-OM and NCEP/ NCAR reanalyses. Cross-validated multiple regression analyses between large-scale circulation and regional temperature and precipitation are performed for observed and simulated data. The impact of circulation is removed from the original temperature and precipitation data, and the trends of circulation-related and circulation-unrelated parts are compared. The circulation-related trends of models and observations show discrepancies owing to differing observed and simulated mid-latitude circulation dynamics, i.e. different temporal evolutions of North Atlantic Oscillation and East Atlantic pattern in winter and East Atlantic Jet and a blocking pattern in summer. Such differences can be related to unknown initial conditions of GCM simulations. In fact, we find strong impacts of initial conditions on mid-latitude circulation dynamics of ECHAM5/ MPI-OM ensemble members over 30-year periods. The agreement between simulated and observed circulation-unrelated trends is generally higher than for original trends indicating that the predictability of this nesting approach increases by removing impacts of mid-latitude circulation variability. We conclude that initial conditions affect climate variability up to the multi-decadal timescale, at least in parts of the globe which are governed by extratropical circulation modes, and hence, hinder the comparability of simulated and observed climate trends over time periods shorter than the timescale dominated by radiative forcing. In the Mediterranean Basin the latter is definitely beyond 30 years. [ABSTRACT FROM AUTHOR]

This article explores the response of convective-scale atmospheric characteristics to surface temperature through the lens of large-domain, cloud-system-resolving model experiments run at radiative convective equilibrium. We note several features reminiscent of the response to surface warming in atmospheric general circulation models. These include an increase in the rain rate that is smaller than the modelled increase in precipitable water, a systematic decrease in sensible heating and an increase in clear-sky cooling. However, in contrast to climate models, we note that tropospheric relative humidity increases and column-integrated water vapour increases at the rate anticipated from the Clausius-Clapeyron relationship, but only when compared with the troposphere mean temperature rather than surface temperature. Also shown are results elucidating the changes in the vertically integrated water budget and the distribution of high precipitation rates shifting toward higher rates. Moist static energy distributions are analyzed and, from these, clouds are implicated in effecting the final equilibrium state of the atmosphere. The results indicate that, while there are aspects of the tropical equilibrium that are represented realistically in current general circulation model climate-change experiments, there are potentially influential local interactions that are sufficiently important as to alter the mean response of the tropical water and energy balance to changes in sea-surface temperature. Convection is shown to dictate the equilibrium state across all scales, including those unresolved in climate models, rather than only responding to surface-induced changes. [ABSTRACT FROM AUTHOR]

In this study, we present a comparison of relative influence of the structure of regional climate model ( RCM) and its driving global climate model ( GCM) on the variance of surface air temperature and precipitation simulated by a multi-model ensemble in the area of the Czech Republic. RCM outputs from the project ENSEMBLES are incorporated and compared to RCM outputs from the project PRUDENCE. We show results for 30-year mean seasonal surface air temperature and precipitation for the reference period (1961-1990) and two future time periods (2021-2050 and 2071-2100), and the changes of these quantities in the future period in comparison to the reference period. We have found that the influence of GCM on the variance of both multi-model ensembles is larger in case of changes of mean seasonal surface air temperature than for the mean values in corresponding time periods. We cannot make any similar general conclusion for precipitation. The results for the ENSEMBLES models are different from the results obtained for the PRUDENCE models in some aspects, especially for mean seasonal surface air temperature and precipitation in the end of the 21st century. Copyright © 2013 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Experimental studies assessing climatic effects on ecological communities have typically applied static warming treatments. Although these studies have been informative, they have usually failed to incorporate either current or predicted future, patterns of variability. Future climates are likely to include extreme events which have greater impacts on ecological systems than changes in means alone. Here, we review the studies which have used experiments to assess impacts of temperature on marine, freshwater and terrestrial communities, and classify them into a set of 'generations' based on how they incorporate variability. The majority of studies have failed to incorporate extreme events. In terrestrial ecosystems in particular, experimental treatments have reduced temperature variability, when most climate models predict increased variability. Marine studies have tended to not concentrate on changes in variability, likely in part because the thermal mass of oceans will moderate variation. In freshwaters, climate change experiments have a much shorter history than in the other ecosystems, and have tended to take a relatively simple approach. We propose a new 'generation' of climate change experiments using down-scaled climate models which incorporate predicted changes in climatic variability, and describe a process for generating data which can be applied as experimental climate change treatments. [ABSTRACT FROM AUTHOR]

The projection of temperature extremes by climate models participating in the Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4) are examined regionally over Australia. Minimum and maximum temperature extremes are defined as the 20 year return value calculated using extreme value theory. Three measures of model evaluation, a means-based, a distribution-based [via probability density functions (PDFs)] and an extreme-based (via the tails of PDFs) method, are used to compare daily model data to observed daily data over various climatic regions for a 20 year period. Model ensembles consisting of the 'better' and 'poorer' models determined by each measure of skill are created for each region. These are compared with an all-model ensemble to examine the difference in more skilled ensemble projections of temperature extremes in the A2 (high emissions) scenario for 2046-2065 and 2081-2100. If either of the distribution-based evaluation methods were used to distinguish models, the higher skilled models projected smaller increases in the 20 year return values than the all-model ensemble for both maximum temperature and minimum temperature. For some regions, the 90% confidence intervals of the better and poorer ensemble ranges did not overlap, indicating that projections are statistically significantly different. We show that the means-based evaluation produces less consistent results to the two distribution-based evaluation methods. We conclude that specific AR4 models, shown to be relatively poor over most regions of Australia by different skill metrics, bias the projected increase in the 20 year temperature extremes towards higher values. We also suggest that performance in simulating the mean climate is an unreliable measure of climate model capacity used to select models for projecting changes in extremes over Australia. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Recent observations show a decrease in the fraction of precipitation falling as snowfall in the western United States. In this work we evaluate a historical and future climate simulation over the Colorado River Basin using a 35 km continuous 111 year simulation (1969-2079) of the Weather Research and Forecasting (WRF) regional climate model with boundary forcing from the Hadley Centre for Climate Prediction and Research/Met Office's Had CM3 model with A2 emission scenario. The focus of this work is to (1) evaluate the simulated spatiotemporal variability of snowfall in the historical period when compared to observations and (2) project changes in snowfall and the fraction of precipitation that falls as snow during the 21st century. We find that the spatial variability in modeled snowfall in the historical period (1981-2005) is realistically represented when compared to observations. The trends of modeled snowfall are similar to the observed trends except at higher elevations. Examining the continuous 111 year simulation, we find the future projections show statistically significant increases in temperature with larger increases in the northern part of the basin. There are statistically insignificant increases in precipitation, while snowfall shows a statistically significant decrease throughout the period in all but the highest elevations and latitudes. The fraction of total precipitation falling as snow shows statistically significant declines in all regions. The strongest decrease in snowfall is seen at high elevations in the southern part of the basin and low elevations in the northern part of the basin. The regions of most intense decreases in snow experience a decline of approximately 50% in snowfall throughout the 111 year simulation period. The regions of strongest declines in snowfall roughly correspond to the region of migration of the zero degree Celsius line and emphasize snowfall dependence on both altitude and latitude. [ABSTRACT FROM AUTHOR]

North Atlantic storms present one of the major weather risks for North West (NW) Europe. This is indeed the case in summer, but the wintertime North Atlantic storm track does not follow this simple picture; instead, it is projected to extend more strongly eastward over NW Europe resulting in an I enhanced i storm risk in this region. [Extracted from the article]

Climate predictions of air temperature in coastal regions represent a great challenge due to the complex interactions among the atmosphere, sea, and land. With approximately 1,200 islands, the Adriatic is a region with a strong land‐sea contrast, land‐atmosphere feedback, and intense air‐sea interaction. Because the Mediterranean has been regarded as a "hot spot" for climate change, regional climate models can be used to provide insight into a more realistic representation of small‐scale weather and climate structure and variability. This advantage is due to the better representation of complex topography, developed coastlines, and land‐sea contrasts, which are important for investigating air temperature trends. The use of regional climate models together with high‐resolution reanalyses and observations in assessments of climate variability and climate change is highly valuable for understanding climate processes at regional scales. The present study focused on air temperature and its trends calculated from measurements and simulated by eight regional climate models from the EURO‐CORDEX database; these data were represented by UERRA reanalyses and E‐OBS gridded data. In the evaluation period (1989–2008), the models' RMSEs were fairly small, in the range 0.5–1.5°C, compared to the historical period (1961–2005), with RMSEs greater than 1.75°C. However, the models showed small absolute trend differences (up to 0.12°C·decade−1 for the historical period). The ensemble means in both periods showed an accuracy improvement of 15–20% compared to the individual models. The models exhibited more success in terms of representing the main statistics and variability of the air temperature structure than in reproducing the temperature trends over 45 years, especially in the northern Adriatic, where there is complex coastal topography and significant seasonal variability in the wind regime. The reanalyses well represented temperature structure but showed less success in explaining the temperature trends than the results from the measured data. [ABSTRACT FROM AUTHOR]