VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process‐based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis‐driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques. Overall, most of the downscaling methods greatly improve (reanalysis or RCM) raw model biases and no approach or technique seems to be superior in general, because there is a large method‐to‐method variability. The main factors most influencing the results are the seasonal calibration of the methods (e.g., using a moving window) and their stochastic nature. The particular predictors used also play an important role in cases where the comparison was possible, both for the validation results and for the strength of the predictor–predictand link, indicating the local variability explained. However, the present study cannot give a conclusive assessment of the skill of the methods to simulate regional future climates, and further experiments will be soon performed in the framework of the EURO‐CORDEX initiative (where VALUE activities have merged and follow on). Finally, research transparency and reproducibility has been a major concern and substantive steps have been taken. In particular, the necessary data to run the experiments are provided at http://www.value-cost.eu/data and data and validation results are available from the VALUE validation portal for further investigation: http://www.value-cost.eu/validationportal.

The performance of Statistical Downscaling (SD) techniques is critically re-assessed with respect to their robust applicability in climate change studies. To this aim, in addition to standard accuracy measures and distributional similarity scores, we estimate the robustness of the methods under warming climate conditions working with anomalous warm historical periods. This validation framework is applied to intercompare the performance of twelve different SD methods (from the analogs, weather typing and regression families) for downscaling minimum and maximum temperatures in Spain. First, we perform a calibration of these methods in terms of both geographical domains and predictor sets; the results are highly dependent on the latter, with optimum predictor sets including information of near-surface temperature (in particular 2 meters temperature), which discriminate appropriately cold episodes related to temperature inversion in the lower troposphere. Although regression methods perform best in terms of correlation, analog and weather generator approaches are more appropriate for reproducing the observed distributions, especially in case of wintertime minimum temperature. However, the latter two families significantly underestimate the temperature anomalies of the warm periods considered in this work. This underestimation is found to be critical when considering the warming signal in the late 21st century as given by a Global Climate Model (the ECHAM5-MPI model). In this case, the different downscaling methods provide warming values with differences in a range of 1 degC, in agreement with the robustness significance values. Therefore, the proposed test for robustness is a promising technique for detecting lack of robustness in statistical downscaling methods for climate change projections.

A new atmospheric-river detection and tracking scheme based on the magnitude and direction of integrated water vapour transport is presented and applied separately over 13 regions located along the west coasts of Europe (including North Africa) and North America. Four distinct reanalyses are considered, two of which cover the entire 20th-century: NOAA-CIRES Twentieth Century Reanalysis v2 (NOAA-20C) and ECMWF ERA-20C. Calculations are done separately for the OND and JFM-season and, for comparison with previous studies, for the ONDJFM-season as a whole. Comparing the AR-counts from NOAA-20C and ERA-20C with a running 31year window looping through 1900-2010 reveals differences in the climatological mean and inter-annual variability which, at the start of the 20th-century, are much more pronounced in western North America than in Europe. Correlating European AR-counts with the North Atlantic Oscillation (NAO) reveals a pattern reminiscent of the well-know precipitation dipole which is stable throughout the entire century. A similar analysis linking western North American AR-counts to the North Pacific index (NPI) is hampered by the aforementioned poor reanalysis agreement at the start of the century. During the second half of the 20th-century, the strength of the NPI-link considerably varies with time in British Columbia and the Gulf of Alaska. Considering the period 1950-2010, AR-counts are then associated with other relevant large-scale circulation indices such as the East Atlantic, Scandinavian, Pacific-North American and West Pacific patterns (EA, SCAND, PNA and WP). Along the Atlantic coastline of the Iberian Peninsula and France, the EA-link is stronger than the NAO-link if the OND season is considered and the SCAND-link found in northern Europe is significant during both seasons. Along the west coast of North America, teleconnections are generally stronger during JFM in which case the NPI-link is significant in any of the five considered subregions, the PNA-link S. Brands,