Assessing climate change impacts on hydrology from an ensemble of regional climate models,model scales and linking methods – a case study on the Lule River basin

This paper investigates how using different regional climate model (RCM) simulations affects climate change impacts on hydrology in northern Europe using an offline hydrological model. Climate change scenarios from an ensemble of seven RCMs, two global climate models (GCMs), two global emissions scenarios and two RCMs of varying resolution were used. A total of 15 climate change simulations were included in studies on the Lule River basin in Northern Sweden. Two different approaches to transfer climate change from the RCMs to hydrological models were tested. A rudimentary estimate of change in hydropower potential on the Lule River due to climate change was also made. The results indicate an overall increase in river flow, earlier spring peak flows and an increase in hydropower potential. The two approaches for transferring the signal of climate change to the hydrological impacts model gave similar mean results, but considerably different seasonal dynamics, a result that is highly relevant for other types of climate change impacts studies.     

Assessment of CMIP5 global model simulations and climate change projections for the 21 st century using a modified Thornthwaite climate classification

A modified Thornthwaite Climate Classification is applied to a 32-member ensemble of CMIP5 GCMs in order to 1) evaluate model performance in the historical climate and 2) assess projected climate change at the end of the 21 ^{s t} century following two greenhouse gas representative concentration pathways (RCP4.5 and RCP8.5). This classification scheme differs from the well-known Köppen approach as it uses potential evapotranspiration for thermal conditions, a moisture index for moisture conditions, and has even intervals between climate classes. The multi-model ensemble (MME) reproduces the main spatial features of the global climate reasonably well, however, in many regions the climate types are too moist. Extreme climate types, such as those found in polar and desert regions, as well as the cool- and cold-wet types of eastern North America and the warm and cool-moist types found in the southern U.S., eastern South America, central Africa and Europe are reproduced best by the MME. In contrast, the cold-dry and cold-semiarid climate types characterizing much of the high northern latitudes and the warm-wet type found in parts of Indonesia and southeast Asia are poorly represented by the MME. Regionally, most models exhibit the same sign in moisture and thermal biases, varying only in magnitude. Substantial changes in climate types are projected in both the RCP4.5 and RCP8.5 scenarios. Area coverage of torrid climate types expands by 11 % and 19 % in the RCP4.5 and RCP8.5 projections, respectively. Furthermore, a large portion of these areas in the tropics will experience thermal conditions which exceed the range of historical values and fall into a novel super torrid climate class. The greatest growth in moisture types in climate zones is among those with dry climates (moisture index values < 0) with increased areas of more than 8 % projected by the RCP8.5 MME.     

Future projections and uncertainty assessment of extreme rainfall intensity in the United States from an ensemble of climate models

Changes in climate are expected to lead to changes in the characteristics extreme rainfall frequency and intensity. In this study, we propose an integrated approach to explore potential changes in intensity-duration-frequency (IDF) relationships. The approach incorporates uncertainties due to both the short simulation periods of regional climate models (RCMs) and the differences in IDF curves derived from multiple RCMs in the North American Regional Climate Change Assessment Program (NARCCAP). The approach combines the likelihood of individual RCMs according to the goodness of fit between the extreme rainfall intensities from the RCMs’ historic runs and those from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data set and Bayesian model averaging (BMA) to assess uncertainty in IDF predictions. We also partition overall uncertainties into within-model uncertainty and among-model uncertainty. Results illustrate that among-model uncertainty is the dominant source of the overall uncertainty in simulating extreme rainfall for multiple locations in the U.S., pointing to the difficulty of predicting future climate, especially extreme rainfall regimes. For all locations a more intense extreme rainfall occurs in future climate; however the rate of increase varies among locations.     

Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP)

We investigate major results of the NARCCAP multiple regional climate model (RCM) experiments driven by multiple global climate models (GCMs) regarding climate change for seasonal temperature and precipitation over North America. We focus on two major questions: How do the RCM simulated climate changes differ from those of the parent GCMs and thus affect our perception of climate change over North America, and how important are the relative contributions of RCMs and GCMs to the uncertainty (variance explained) for different seasons and variables? The RCMs tend to produce stronger climate changes for precipitation: larger increases in the northern part of the domain in winter and greater decreases across a swath of the central part in summer, compared to the four GCMs driving the regional models as well as to the full set of CMIP3 GCM results. We pose some possible process-level mechanisms for the difference in intensity of change, particularly for summer. Detailed process-level studies will be necessary to establish mechanisms and credibility of these results. The GCMs explain more variance for winter temperature and the RCMs for summer temperature. The same is true for precipitation patterns. Thus, we recommend that future RCM-GCM experiments over this region include a balanced number of GCMs and RCMs.     

Estimating Climate-Induced Change in Soil Moisture at the LandscapeScale: An Application to Five Areas of Ecological Interest in the U.K.

This paper presents an indication of the possible effects of climate change on monthly mean soil moisture at a fine spatial resolution (50 m) over the scale of a landscape (100–250 km²). Soil moisture is modelledusing daily time series of rainfall and potential evapotranspiration to drive a simple hydrological model operating on individual hillslopes and explicitly including, on a conceptual level, the lateral movement of water. Climate change is represented by the UKTR scenario and model results are provided at two time slices (the years 2030–2040 and 2060–2070) for five areasof ecological interest, forming a north-south transect across the U.K. The results are given in terms of the distribution of the monthly mean soil moisture change by soil type. The spread of values reflects the effect of the topographic control on the lateral movement of water. The results show a small increase in wetness at the Cairngorm site, a very slight decrease in summer soil moisture at the Moor House site and a very marked fall in soil moisture for the three more southerly sites. The importance of soil type in determining the availability of water to plants, the changing areal extent above specified soil moisture thresholds, and the implications for ecological change and conservation are discussed.     

Evaluation of climate change over the continental United States using a moisture index

This paper uses a modified form of Thornthwaite’s moisture index to better quantify climate variability by integrating the effects of temperature and precipitation. Using the moisture index, trends were evaluated over the last 112 years (1895–2006), when unique changes in temperature and precipitation have been documented to have occurred. In addition, data on potential evapotranspiration and the moisture index were used to investigate changing climate and vegetation regions. The results show that the eastern half of the country has been getting wetter, even as temperatures have continued to increase in many areas. In particular, conditions have become wetter in the South, Northeast, and East North Central regions. The changing climate is illustrated by computing climate and vegetation regions for three 30-year periods (1910–1939, 1940–1969, and 1970–1999). Climate regions based on the moisture index show an expansion of the Humid region (where precipitation vastly exceeds climatic demands for water) across the East as well as a westward shift in the zero moisture index line. In terms of vegetation zones, the most dramatic change occurs across the Midwestern prairie peninsula where the wetter conditions lead to a westward expansion of conditions favorable for oak–hickory–pine vegetation.     

The effect of global climate change, population distribution, and climate mitigation on building energy use in the U.S. and China

Climate change will affect the energy system in a number of ways, one of which is through changes in demands for heating and cooling in buildings. Understanding the potential effect of climate change on heating and cooling demands requires taking into account not only the manner in which the building sector might evolve over time, but also important uncertainty about the nature of climate change itself. In this study, we explore the uncertainty in climate change impacts on heating and cooling requirement by constructing estimates of heating and cooling degree days (HDD/CDDs) for both reference (no-policy) and 550 ppmv CO₂ concentration pathways built from three different Global Climate Models (GCMs) output and three scenarios of gridded population distribution. The implications that changing climate and population distribution might have for building energy consumption in the U.S. and China are then explored by using the results of HDD/CDDs as inputs to a detailed, building energy model, nested in the long-term global integrated assessment framework, Global Change Assessment Model (GCAM). The results across the modeled changes in climate and population distributions indicate that unabated climate change would cause building sector’s final energy consumption to decrease modestly (6 % decrease or less depending on climate models) in both the U.S. and China by the end of the century as decreased heating consumption more than offsets increased cooling using primarily electricity. However, global climate change virtually has negligible effect on total CO₂ emissions in the buildings sector in both countries. The results also indicate more substantial implications for the fuel mix with increases in electricity and decreases in other fuels, which may be consistent with climate mitigation goals. The variation in results across all scenarios due to variation of population distribution is smaller than variation due to the use of different climate models.     

Sensitivity analysis of 18 different potential evapotranspiration models to observed climatic change at German climate stations

Potential evapotranspiration models very often are important part of hydrological catchment models to calculate potential evapotranspiration (PET) which then is used to estimate actual evapotranspiration considering the soil moisture status. As many different approaches exist, the question arises in which way the choice of the PET model affects the impact of climate change on the calculated water balance? Therefore, 18 different PET models were compared with respect to their sensitivity to observed climate change. Long-term climate data of six German climate stations were used to identify changes in the climate data itself and changes in the calculated PET. The results show that all investigated PET models are sensitive to significant trends in climate data. However, it is also shown that all models show different sensitivities, and that the sensitivities cannot be grouped in terms of different types of PET models such as the aerodynamic concept, radiation or temperature based approaches and combination equations. Predominantly, the variability within a group of models of the same type is comparable to the variability between different model types. Therefore it can be concluded that PET models should be validated in a regional context before they are applied to a certain region within a climate change study despite the poor availability of long-term PET measurements.     

Evaluation of the CORDEX regional climate models performance in simulating climate conditions of two catchments in Upper Blue Nile Basin

This study was targeted at evaluating the performance of six Regional Climate Models (RCMs) used in Coordinated Regional Climate Downscaling Experiment (CORDEX). The evaluation is on the bases of how well the RCMs simulate the seasonal mean climatology, interannual variability and annual cycles of rainfall, maximum and minimum temperature over two catchments in western Ethiopia during the period 1990–2008. Observed data obtained from the Ethiopian National Meteorological Agency was used for performance evaluation of the RCMs outputs. All Regional Climate Models (RCMs) have simulated seasonal mean annual cycles of precipitation with a significant bias shown on individual models; however, the ensemble mean exhibited better the magnitude and seasonal rainfall. Despite the highest biases of RCMs in the wet season, the annual cycle showed the prominent features of precipitation in the two catchments. In many aspects, CRCM5 and RACMO22 T simulate rainfall over most stations better than the other models. The highest biases are associated with the highest error in simulating maximum and minimum temperature with the highest biases in high elevation areas. The rainfall interannual variability is less evident in Finchaa with short rainy season experiencing a larger degree of interannual variability. The differences in performance of the Regional Climate Models in the two catchments show that all the available models are not equally good for particular locations and topographies. In this regard, the right regional climate models have to be used for any climate change impact study for local-scale climate projections.     

High-resolution simulations for Vietnam - methodology and evaluation of current climate

To assist the government of Vietnam in its efforts to better understand the impacts of climate change and prioritise its adaptation measures, dynamically downscaled climate change projections were produced across Vietnam. Two Regional Climate Models (RCMs) were used: CSIRO’s variable-resolution Conformal-Cubic Atmospheric Model (CCAM) and the limited-area model Regional Climate Model system version 4.2 (RegCM4.2). First, global CCAM simulations were completed using bias- and variance-corrected sea surface temperatures as well as sea ice concentrations from six Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models. This approach is different from other downscaling approaches as it does not use any atmospheric fields from the GCMs. The global CCAM simulations were then further downscaled to 10 km using CCAM and to 20 km using RegCM4.2. Evaluations of temperature and precipitation for the current climate (1980-2000) were completed using station data as well as various gridded observational datasets. The RCMs were able to reproduce reasonably well most of the important characteristics of observed spatial patterns and annual cycles of temperature. Average and minimum temperatures were well simulated (biases generally less than 1oC), while maximum temperatures had biases of around 1oC. For precipitation, although the RCMs captured the annual cycle, RegCM4.2 was too dry in Oct.-Nov. (-60% bias), while CCAM was too wet in Dec.- Mar. (130% bias). Both models were too dry in summer and too wet in winter (especially in northern Vietnam). The ability of the ensemble simulations to capture current climate increases confidence in the simulations of future climate.     

Evaluation of Hydrologically Relevant PCM Climate Variables and Large-Scale Variability over the Continental U.S.

The ability of the Parallel Climate Model (PCM) to reproduce the mean and variability of hydrologically relevant climate variables was evaluated by comparing PCM historical climate runs with observations over temporal scales from sub-daily to annual. The domain was the continental U.S, and the model spatial resolution was T42 (about 2.8 degrees latitude by longitude). The climate variables evaluated include precipitation, surface air temperature, net surface solar radiation, soil moisture, and snow water equivalent. The results show that PCM has a winter dry bias in the Pacific Northwest and a summer wet bias in the central plains. The diurnal precipitation variation in summer is much stronger than observed, with an afternoon maximum in summer precipitation over much of the U.S. interior, in contrast with an observed nocturnal maximum in parts of the interior. PCM has a cold bias in annual mean temperature over most of the U.S., with deviations as large as ?8 K. The PCM daily temperature range is lower than observed, especiallyin the central U.S. PCM generally overestimates the net solar radiation over most of the U.S, although the diurnal cycle is simulated well in spring, summer and winter. In autumn PCM has a pronounced noontime peak in solar radiation that differs by 5–10% from observations. PCM'ssimulated soil moisture is less variable than that of a sophisticated land-surface hydrology model, especially in the interior of the country. PCM simulates the wetter conditions over the southeastern U.S. and California during warm (El Niño) events, but shifts the drier conditions in the PacificNorthwest northward and underestimates their magnitude. The temperature response to the North Pacific Oscillation is generally captured by PCM, but the amplitude of this response is overestimated by a factor of about two.     

Comparisons of Low-Level Circulation Characteristics between ECHAM5/MPI-OM Results and NCEP/NCAR Re-Analysis Data in East Asia

Regional climate models(RCMs) can provide far more precise information than general circulation models(GCMs).However,RCMs depend on GCM results or re-analysis products providing boundary conditions,especially for future climate scenarios.Meanwhile,the capacity of RCMs to reproduce precipitation is strongly connected to its performance on circulation and moisture transport simulations in the low troposphere,which is the key problem with RCMs at present.In the Regional Climate Model Inter-comparison Project for East Asia(RMIP III),the results of ECHAM5/MPI-OM(the European Centre-Hamburg model version 5/Max Planck Institute Ocean Model,simplified as E5OM here) are used to drive RCMs for the past(1978?2000) climate simulation and future(2038?70) climate scenarios.Therefore,it is necessary to test E5OM’s ability to represent atmospheric circulation,which defines the large-scale circulation for RCMs.Here,comparisons between the E5OM results and NCEP/NCAR(simplified as NCEP) re-analysis data in the low troposphere for the years 1978 to 2000 are performed.The results show that E5OM results can generally reproduce atmospheric circulations in the low troposphere.However,differences can be detected in East Asian summer and winter monsoon simulations.For summer,there is an anti-cyclone circulation for the difference of wind vector at 850 hPa in Southeast China,the Indo-China Peninsula,the South China Sea,and the northwestern Pacific.For winter,due to the weaker northwesterly wind in Northeast Asia,the northeasterly wind from the Indo-China Peninsula to Taiwan in E5OM extends northward with greater intensity than that in NCEP.These differences will have a considerable influence on the low level atmospheric circulation and water vapor transport as well as the location and intensity of the precipitation.Therefore,when E5OM results are to be used as initial and boundary conditions to drive RCMs,the differences between NCEP and E5OM should be considered.     

Climate change projections for the Texas High Plains and Rolling Plains

Potential changes in future climate in the Texas Plains region were investigated in the context of agriculture by analyzing three climate model projections under the A2 climate scenario (medium–high emission scenario). Spatially downscaled historic (1971–2000) and future (2041–2070) climate datasets (rainfall and temperature) were downloaded from the North American Regional Climate Change Assessment Program (NARCCAP). Climate variables predicted by three regional climate models (RCMs) namely the Regional Climate Model Version3–Geophysical Fluid Dynamics Laboratory (RCM3-GFDL), Regional Climate Model Version3–Third Generation Coupled Global Climate Model (RCM3-CGCM3), and Canadian Regional Climate Model–Community Climate System Model (CRCM-CCSM) were evaluated in this study. Gaussian and Gamma distribution mapping techniques were employed to remove the bias in temperature and rainfall data, respectively. Both the minimum and maximum temperatures across the study region in the future showed an upward trend, with the temperatures increasing in the range of 1.9 to 2.9 °C and 2.0 to 3.2 °C, respectively. All three climate models predicted a decline in rainfall within a range of 30 to 127 mm in majority of counties across the study region. In addition, they predicted an increase in the intensity of extreme rainfall events in the future. The frost-free season as predicted by the three models showed an increase by 2.6–3.4 weeks across the region, and the number of frost days declined by 17.9 to 30 %. Overall, these projections indicate considerable changes to the climate in the Texas Plains region in the future, and these changes could potentially impact agriculture in this region.

     

区域气候模拟研究及其应用进展

区域气候模拟研究在过去十几年里取得了显著的进步。经过广泛的发展和不断的检验,区域气候模式现在已经成为气候研究和业务预报的重要工具。目前已经发表了很多令人鼓舞的结果,其中包括过去极端气候事件的模拟,当前气候发展演变和未来气候变化的预测,特别是对月和季节尺度气候的模拟与预测。通过对高分辨率和动力连续的区域气候模式结果的分析,人们对于周-季节时间尺度的各种物理过程,包括陆面和水文过程、边界层、云和降水、云-辐射相互作用的认识也在不断的深入。然而,区域气候是多尺度扰动(如中尺度、天气尺度、行星尺度扰动)和多圈层系统(如大气圈、生物圈、水圈、冰雪圈、陆面)相互作用的结果,同时物理过程本身具有不确定性,人们对一些复杂的物理过程,特别是土壤湿度作用以及云-气候反馈过程也缺乏深刻的理解,因此该领域的研究还面临着很多挑战。作者重点总结并评述了区域气候模式对现在和未来区域气候模拟、极端天气和气候事件模拟、物理过程研究、短期气候预测几方面应用的研究进展,最后讨论了区域气候模式发展在上述各方面,特别是周-次季节时间尺度区域天气和气候的模拟与预测所面临的挑战和应用前景。     

A Perspective on Water Resources in China: Interactions between Climate Change and Soil Degradation

Water is one of the most critical resources in China. Climate change and soil degradation will be two major, interrelated environmental challenges faced by managers of water resources in coming decades. In this study, we used a water-balance model and updated databases to assess the interacting impacts of climate change and soil degradation on Chinas future water resources. We plotted the spatial pattern of changes in actual and potential evapotranspiration, soil moisture deficits, and surface runoff across China in the 2020s using a resolution of 0.5° latitude and longitude under scenarios based on climate change, soil degradation, and a combination of the two. The results showed that climate change would affect the magnitude and spatial pattern of water resources on a national scale. Some regions in central, southwestern, and northeastern China would become more vulnerable to disastrous drought and floods as a result of soil degradation. Under the combined impacts of climate change and soil degradation, soil moisture deficits would increase most in central, western, and southwestern China; surface runoff would increase most in southeastern China. More detailed process-based models are needed to capture feedback mechanisms more effectively.     

Impacts of bias correction of lateral boundary conditions on regional climate projections in West Africa

Biases existing in the lateral boundary conditions (LBCs) influence climate simulations in regional climate models (RCMs). Correcting the biases in global climate model (GCM)-produced LBCs before running RCMs was proposed in previous studies as a possible way to reduce the GCM-related model dependence of future climate projections using RCMs. In this study the ICTP Regional Climate Model Version 4 (RegCM4) is used to investigate the impact of LBC bias correction on projected future changes of regional climate in West Africa. To accomplish this, two types of present versus future simulations are conducted using RegCM4: a control type where both the present and future LBCs are derived directly from the GCM output (as is done in most regional climate downscaling studies); an experiment type where the present-day LBCs are from reanalysis data and future LBCs are derived by combining the reanalysis data and the GCM-projected LBC changes. For each type of simulations, three different sets of LBCs are experimented on: 6-hourly synoptic forcing directly from the reanalysis or GCM, 6-hourly data interpolated from monthly climatology (without diurnal cycle), and 6-hourly data interpolated from the month-specific climatology of diurnal cycles. It is found that the simulations using different LBCs produce similar present-day summer rainfall patterns, but the predicted future changes differ significantly depending on how the LBC bias correction is treated. Specifically, both the bias correction applied at the synoptic scale and the bias correction applied to the monthly interpolated LBCs without diurnal cycle produce a spurious drying signal caused by physical inconsistency in the corrected future LBCs. Interpolated monthly LBCs with diurnal cycle alleviate the problem to a large extent. These results suggest that using bias-corrected LBCs to drive regional climate models may not guarantee reliable future projections although reasonable present climate can be simulated. Physical inconsistencies may be contained in the bias-corrected LBCs, increasing the uncertainties of RCM-produced future projections.     

The WAMME regional model intercomparison study

Results from five regional climate models (RCMs) participating in the West African Monsoon Modeling and Evaluation (WAMME) initiative are analyzed. The RCMs were driven by boundary conditions from National Center for Environmental Prediction reanalysis II data sets and observed sea-surface temperatures (SST) over four May–October seasons, (2000 and 2003–2005). In addition, the simulations were repeated with two of the RCMs, except that lateral boundary conditions were derived from a continuous global climate model (GCM) simulation forced with observed SST data. RCM and GCM simulations of precipitation, surface air temperature and circulation are compared to each other and to observational evidence. Results demonstrate a range of RCM skill in representing the mean summer climate and the timing of monsoon onset. Four of the five models generate positive precipitation biases and all simulate negative surface air temperature biases over broad areas. RCM spatial patterns of June–September mean precipitation over the Sahel achieve spatial correlations with observational analyses of about 0.90, but within two areas south of 10°N the correlations average only about 0.44. The mean spatial correlation coefficient between RCM and observed surface air temperature over West Africa is 0.88. RCMs show a range of skill in simulating seasonal mean zonal wind and meridional moisture advection and two RCMs overestimate moisture convergence over West Africa. The 0.5° computing grid enables three RCMs to detect local minima related to high topography in seasonal mean meridional moisture advection. Sensitivity to lateral boundary conditions differs between the two RCMs for which this was assessed. The benefits of dynamic downscaling the GCM seasonal climate prediction are analyzed and discussed.     

Climate Change and People-Caused Forest Fire Occurrence in Ontario

Climate change that results from increasing levels of greenhouse gases in the atmosphere has the potential to increase temperature and alter rainfall patterns across the boreal forest region of Canada. Daily output from the Canadian Climate Centre coupled general circulation model (GCM) and the Hadley Centre's HadCM3 GCM provided simulated historic climate data and future climate scenarios for the forested area of the province of Ontario, Canada. These models project that in climates of increased greenhouse gases and aerosols, surface air temperatures will increase while seasonal precipitation amounts will remain relatively constant or increase slightly during the forest fire season. These projected changes in weather conditions are used to predict changes in the moisture content of forest fuel, which influences the incidence of people-caused forest fires. Poisson regression analysis methods are used to develop predictive models for the daily number of fires occurring in each of the ecoregions across the forest fire management region of Ontario. This people-caused fire prediction model, combined with GCM data, predicts the total number of people-caused fires in Ontario could increase by approximately 18% by 2020–2040 and50% by the end of the 21st century.     

Validation of precipitation over Japan during 1985–2004 simulated by three regional climate models and two multi-model ensemble means

We dynamically downscaled Japanese reanalysis data (JRA-25) for 60 regions of Japan using three regional climate models (RCMs): the Non-Hydrostatic Regional Climate Model (NHRCM), modified RAMS version 4.3 (NRAMS), and modified Weather Research and Forecasting model (TWRF). We validated their simulations of the precipitation climatology and interannual variations of summer and winter precipitation. We also validated precipitation for two multi-model ensemble means: the arithmetic ensemble mean (AEM) and an ensemble mean weighted according to model reliability. In the 60 regions NRAMS simulated both the winter and summer climatological precipitation better than JRA-25, and NHRCM simulated the wintertime precipitation better than JRA-25. TWRF, however, overestimated precipitation in the 60 regions in both the winter and summer, and NHRCM overestimated precipitation in the summer. The three RCMs simulated interannual variations, particularly summer precipitation, better than JRA-25. AEM simulated both climatological precipitation and interannual variations during the two seasons more realistically than JRA-25 and the three RCMs overall, but the best RCM was often superior to the AEM result. In contrast, the weighted ensemble mean skills were usually superior to those of the best RCM. Thus, both RCMs and multi-model ensemble means, especially multi-model ensemble means weighted according to model reliability, are powerful tools for simulating seasonal and interannual variability of precipitation in Japan under the current climate.     

A study on combining global and regional climate model results for generating climate scenarios of temperature and precipitation for the Netherlands

Climate scenarios for the Netherlands are constructed by combining information from global and regional climate models employing a simplified, conceptual framework of three sources (levels) of uncertainty impacting on predictions of the local climate. In this framework, the first level of uncertainty is determined by the global radiation balance, resulting in a range of the projected changes in the global mean temperature. On the regional (1,000–5,000 km) scale, the response of the atmospheric circulation determines the second important level of uncertainty. The third level of uncertainty, acting mainly on a local scale of 10 (and less) to 1,000 km, is related to the small-scale processes, like for example those acting in atmospheric convection, clouds and atmospheric meso-scale circulations—processes that play an important role in extreme events which are highly relevant for society. Global climate models (GCMs) are the main tools to quantify the first two levels of uncertainty, while high resolution regional climate models (RCMs) are more suitable to quantify the third level. Along these lines, results of an ensemble of RCMs, driven by only two GCM boundaries and therefore spanning only a rather narrow range in future climate predictions, are rescaled to obtain a broader uncertainty range. The rescaling is done by first disentangling the climate change response in the RCM simulations into a part related to the circulation, and a residual part which is related to the global temperature rise. Second, these responses are rescaled using the range of the predictions of global temperature change and circulation change from five GCMs. These GCMs have been selected on their ability to simulate the present-day circulation, in particular over Europe. For the seasonal means, the rescaled RCM results obey the range in the GCM ensemble using a high and low emission scenario. Thus, the rescaled RCM results are consistent with the GCM results for the means, while adding information on the small scales and the extremes. The method can be interpreted as a combined statistical–dynamical downscaling approach, with the statistical relations based on regional model output.