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Statistical downscaling methods (SDMs) are techniques used to downscale and/or bias‐correct climate model results to regional or local scales. The European network VALUE developed a framework to evaluate and inter‐compare SDMs. One of VALUE's experiments is the perfect predictor experiment that uses reanalysis predictors to isolate downscaling skill. Most evaluation papers for SDMs employ simple statistical diagnostics and do not follow a process‐based rationale. Thus, in this paper, a process‐based evaluation has been conducted for the more than 40 participating model output statistics (MOS, mostly bias correction) and perfect prognosis (PP) methods, for temperature and precipitation at 86 weather stations across Europe. The SDMs are analysed following the so‐called "regime‐oriented" technique, focussing on relevant features of the atmospheric circulation at large to local scales. These features comprise the North Atlantic Oscillation, blocking and selected Lamb weather types and at local scales the bora wind and the western Iberian coastal‐low level jet. The representation of the local weather response to the selected features depends strongly on the method class. As expected, MOS is unable to generate process sensitivity when it is not simulated by the predictors (ERA‐Interim). Moreover, MOS often suffers from an inflation effect when a predictor is used for more than one station. The PP performance is very diverse and depends strongly on the implementation. Although conditioned on predictors that typically describe the large‐scale circulation, PP often fails in capturing the process sensitivity correctly. Stochastic generalized linear models supported by well‐chosen predictors show improved skill to represent the sensitivities. [ABSTRACT FROM AUTHOR]

There are two main approaches to downscale global climate projections: dynamical and statistical downscaling. Both families have been widely evaluated, but intercomparison studies between the two families are scarce, and usually limited to temperature and precipitation. In this work, we present a comparison between a statistical downscaling model (SDM) based on machine learning and six regional climate models (RCMs) from EURO‐CORDEX, for five variables of interest: temperature, precipitation, wind, humidity and solar radiation under present climatic conditions. The study is conducted at a continental scale over Europe, with a spatial resolution of 0.11° and daily data. Both the SDM and the RCMs are driven by the ERA‐Interim reanalysis, and observations are taken from the gridded dataset E‐OBS. Several aspects have been evaluated: daily series, mean values and extremes, spatial patterns and also temporal aspects. Additionally, a multivariable index (fire weather index) derived from the fundamental variables has been included. The SDM has better scores than the RCMs for all the evaluated metrics with only a few exceptions, mainly related to an underestimation of the variance. After bias correction, both the SDM and the six RCMs present similar results, with no significant differences among them. Results presented here, combined with the low computational expense of SDMs and the limited availability of RCMs over some CORDEX domains, should motivate the consideration of statistical downscaling at the same level as RCMs by official providers of regional information, and its inclusion in reference sites. Nonetheless, further analysis on crucial aspects such as the impact on long‐term trends or the sensitivity of different methods to being driven by global climate models, is needed. [ABSTRACT FROM AUTHOR]

Classifications by self‐organizing maps (SOMs) have been used to study links between extreme weather events and atmospheric circulation. However, SOMs may often provide a too generalized image of circulation, which is too coarse to properly resolve meteorological extremes. Several approaches have been used to circumvent this limitation, including an increase in the number of nodes and/or relaxation of the stress on topological ordering of nodes, through adjusting the neighbourhood radius. Here, we (1) test whether weighting of training fields based on extremity of their circulation could lead to SOMs that would better represent circulation extremes without impairing the topological ordering of nodes, and (2) we investigate whether Sammon maps (SM), which have so far been limited to validation of SOMs in climatology, could provide a viable alternative to SOMs in studying weather extremes. We show that removing a certain percentage of circulation fields closest to the climatological mean has a greater effect on the representation of circulation extremes than more than doubling the SOM array size. Additionally, this step allows for decreasing the neighbourhood radius, the parameter responsible for topological ordering, which would not otherwise be possible without impairing the topology of the map. The SOMs are compared with two newly developed methods that discretize SMs based on arbitrarily chosen and distribution‐based bins. We demonstrate the usefulness of both methods in climate model validation and show that both methods are able to capture circulation variability in its extremes in more detail than the SOMs. The distribution‐based method does not suffer from sample‐size issues and outperforms all other classifications in extracting ERA5 patterns leading to extreme temperature minima over central Europe. Our results suggest that classifications based on SM could be a viable alternative to SOMs when SOMs lack the ability to discriminate circulation outliers leading to temperature extremes. [ABSTRACT FROM AUTHOR]

Globally, the impacts of climate change can vary across different regions. This study uses a probability framework to evaluate recent historical (1976–2016) and near‐future projected (until 2049) climate change across Europe using Climate Research Unit and ensemble climate model datasets (under RCPs 2.6 and 8.5). A historical assessment shows that although the east and west of the domain experienced the largest and smallest increase in temperature, changes in precipitation are not as smooth as temperature. Results indicate that the maximum changes between distributions of the variables (temperature and precipitation) mainly occur at extreme percentiles (e.g., 10% and 90%). A group analysis of four subregions of Europe, namely east (G1), north (G2), west/south (G3), and UK/Ireland (G4), shows that G1 and G4 are expected to have the largest increase in temperature and precipitation extremes, respectively. Although maximum increases in temperature in G3 and G4 occur at larger percentiles, G1 and G2 experience maximum increases at both large and small percentiles. The maximum increase of precipitation over the study domain, however, occurs mainly at larger extremes. To quantify changes in the distribution of projection (2020–2049) relative to the historical reference (1976–2005), two measures are defined, namely a change in occurrences (KS statistic) and intensities at different quantiles (Δ). Results confirm that the temperature distribution tends to shift to higher temperatures. Changes in distribution and extremes of precipitation are spatially variable. Furthermore, extremes are expected to be intensified under RCP 8.5. The quantile analysis and defined measures reveal changes in the entire probability distribution, reflecting possible climate changes in the future. [ABSTRACT FROM AUTHOR]

Evaluating the capability of global climate models (GCMs) in reproducing the historical records represents a way of building confidence in their capability for future projections. Among the different ways of evaluating them, here we focus on their capability in reproducing the temporal clustering of heavy precipitation events across Europe in light of four climate modes [the Arctic Oscillation (AO), the North Atlantic Oscillation (NAO), the East Atlantic (EA) pattern, and the Scandinavia pattern (SCAND)], and GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and the large ensemble runs using the Community Earth System Model (CESM). We use a peak over threshold (POT) approach to identify heavy precipitation events, and Cox regression to relate the occurrence of these events to the climate modes. We find that the GCMs can capture the temporal clustering in heavy precipitation across Europe as a function of these four climate modes; moreover, our findings indicate the GCMs can better reproduce the relationship between heavy precipitation and AO/SCAND, than NAO/EA. Comparing the results based on CMIP5 models and CESM, we find that the inter‐model uncertainties are larger than the intra‐model ones in most of the cases, even though CESM tends to have a poorer performance for EA; this shortcoming for CESM is likely due to the pattern of the Z500 anomalies which is different from the reference data when EA is in the positive phase, affecting the transport of moisture across Europe. [ABSTRACT FROM AUTHOR]

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

Projections of climate change impacts over Europe are derived using a new quantile–quantile adjustment method. E‐OBS high‐resolution gridded data sets of daily observed precipitation and 2‐m surface minimum and maximum temperatures have been used as the current climate baseline. For projections, the same meteorological variables have been obtained from a set of regional climate models (RCMs) integrated in the EURO‐CORDEX project, and by considering the RCP4.5 and RCP8.5 future emissions scenarios. To enhance the reliability of RCM data at local scale, new developments of a previous quantile–quantile adjustment have been applied to the simulated regional scenarios. This method focuses not only on the bulk spectrum of the cumulative distribution functions but also on its tails. Results show an overall improvement in reproducing the present climate baseline when using calibrated series instead of raw RCM outputs. Next, we have used these locally adjusted series to quantify the climate change signal through a number of annual and seasonal indicators. A significant increase of the minimum and maximum temperatures in all seasons is projected over Europe, being more marked in the Mediterranean for summer and autumn. Prospects on future seasonal and annual changes in precipitation are more diverse, showing an overall decrease in southern Europe and the Mediterranean, while precipitation is expected to increase towards the north of the continent. With these sources of information at hand, including and accounting for the identification of the most vulnerable geographical areas, policy makers and stakeholders can respond more effectively to the future challenges imposed by climate change. [ABSTRACT FROM AUTHOR]

ABSTRACT Ensembles of high-resolution regional climate model ( RCM) simulations are crucial for assessing regional climate change and the associated uncertainties. This article presents an RCM ensemble generation technique which explores uncertainties arising from the positioning of synoptic systems in the large-scale atmospheric forcing by shifting the atmospheric fields from global climate model ( GCM) runs horizontally. Here, we discuss how the so-called Atmospheric Forcing Shifting ( AFS) affects temperature and precipitation over Europe for the period 1980-1984. We use ERA-40 reanalysis data in which the atmospheric fields are shifted to each direction by 25 and 50 km, respectively, to run RCM simulations with COSMO-CLM at 50-km resolution. The analysis of the AFS ensemble includes comparisons with E- OBS observations and COSMO-CLM runs driven by different GCMs. AFS has an evident effect on the spatiotemporal distributions of temperature and particularly precipitation, which is most pronounced during hydrological summer (May to October) when spatial weather patterns are more variable. Furthermore, AFS produces realistic changes in the likelihood and intensity of extreme precipitation. The changes induced by AFS depend strongly on orography, i.e. precipitation increases are likely to occur where moist air masses are shifted towards the windward mountain side and vice versa. Thus, increasing the RCM ensemble spread by means of AFS is a simple and useful method for sampling observed climate statistics and assessing the variability and changes in mean and extreme climate. [ABSTRACT FROM AUTHOR]

ABSTRACT As both global (GCM) and regional (RCM) climate models have their own advantages, the most comprehensive picture of changes in precipitation and their uncertainty ranges can be achieved by comparing the results of both model categories. Here we have evaluated seasonal changes in indices representing excess or scarcity of precipitation in Europe on the basis of simulations performed with ten GCMs with a reasonably high spatial resolution and, for comparison, with five RCMs driven by one and the same GCM. We found no fundamental differences between the GCMs and RCMs in the projected tendency towards a more extreme precipitation climate, characterized by increases both in indices representing wet conditions and also dry conditions. Most evidently in the Northern European summer and Southern European winter, the differences in the responses of the various indices between the present sets of GCMs and RCMs could be explained by the dissimilar changes in seasonal mean precipitation. The scatter among the projected changes was in general much smaller for the RCM than for the GCM simulations. The projections of individual RCMs mainly fall within the interval determined by the GCM projections. This indicates that the GCM ensemble yielded more comprehensive estimates for the uncertainty ranges in the extreme precipitation indices. [ABSTRACT FROM AUTHOR]

ABSTRACT Through the use of the climatic classification of Köppen-Trewartha (K-T), the ability to reproduce the current climate of Europe has been shown for an ensemble of 15 regional climate model simulations nested in six global climate models. Depending on the simulation, between 55.4 and 81.3% of the grid points are in agreement with observations regarding the location of climate types in current climate simulations (1971-2000). In this respect, the result of the ensemble of 15 simulations is better than that of any individual model, with 83.5% of the grid points in agreement with observations. K-T classification has also been used to analyse the projected climate change over the 21st century under the SRES-A1B emissions scenario. It was found that 22.3% of the grid points in the domain change their climate by the period 2021-2050 compared to current climate and 48.1% change by 2061-2090. The climate shifts affecting the biggest extensions are projected in Central Europe and Fennoscandia, but other smaller areas suffer more intense changes which potentially are more dangerous to vegetation and ecosystems. Generally, these changes occur at a sustained rate throughout the century, reaching speeds of up to 90 × 103 km2 decade−1 in the retreat or expansion of some climates. [ABSTRACT FROM AUTHOR]