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 Mediterranean coast of Spain often experiences intense rainfall, sometimes reaching remarkable amounts of more than 400 mm in one day. The aim of this work is to study possible changes of extreme precipitation in Spain for this century, simulated from several Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. Eighteen climate projections (nine models under RCP4.5 and nine RCP8.5 scenarios) were downscaled using a two‐step analogue/regression statistical method. We have selected 144 rain gauges as the rainiest of a network by using a threshold of 250 mm in one day for a return period of 100 years. Observed time‐series have been extended using the ERA40 reanalysis and have subsequently been used to correct the climate projections according to a parametric quantile–quantile method. Five theoretical distributions (Gamma, Weibull, Classical Gumbel, Reversed Gumbel and Log‐logistic) have been used to fit the empirical cumulative functions (entire curves, not only the upper tail) and to estimate the expected precipitation according to several return periods: 10, 20, 50 and 100 years. Results in the projected changes for 2051–2100 compared to 1951–2000 are similar (in terms of sign and value) for the four return periods. The analysed climate projections show that changes in extreme rainfall patterns will be generally less than the natural variability. However, possible changes are detected in some regions: decreases are expected in a few kilometres inland, but with a possible increase in the coastline of southern Valencia and northern Alicante, where the most extreme rainfall was recorded. These results should be interpreted with caution because of the limited number of climate projections; anyway, this work shows that the developed methodology is useful for studying extreme rainfall under several climate scenarios.

This paper addresses the determination of the realized thermal niche and the effects of climate change on the range distribution of two brown trout populations inhabiting two streams in the Duero River basin (Iberian Peninsula) at the edge of the natural distribution area of this species. For reaching these goals, new methodological developments were applied to improve reliability of forecasts. Water temperature data were collected using 11 thermographs located along the altitudinal gradient, and they were used to model the relationship between stream temperature and air temperature along the river continuum. Trout abundance was studied using electrofishing at 37 sites to determine the current distribution. The Representative Concentration Pathways RCP4·5 and RCP8·5 change scenarios adopted by the International Panel of Climate Change for its Fifth Assessment Report were used for simulations and local downscaling in this study. We found more reliable results using the daily mean stream temperature than maximum daily temperature and their respective 7 days moving average to determine the distribution thresholds. Thereby, the observed limits of the summer distribution of brown trout were linked to thresholds between 18·1 and 18·7 °C. These temperatures characterize a realized thermal niche narrower than the physiological thermal range. In the most unfavourable climate change scenario, the thermal habitat loss of brown trout increased to 38% (Cega stream) and 11% (Pirón stream) in the upstream direction at the end of the century; however, at the Cega stream, the range reduction could reach 56% due to the effect of a ‘warm‐window’ opening in the piedmont reach. Copyright © 2015 John Wiley & Sons, Ltd.

Climate changes affect aquatic ecosystems by altering temperatures and precipitation patterns, and the rear edges of the distributions of cold-water species are especially sensitive to these effects. The main goal of this study was to predict in detail how changes in air temperature and precipitation will affect streamflow, the thermal habitat of a cold-water fish (the brown trout, Salmo trutta), and the synergistic relationships among these variables at the rear edge of the natural distribution of brown trout. Thirty-one sites in 14 mountain rivers and streams were studied in central Spain. Models of streamflow were built for several of these sites using M5 model trees, and a non-linear regression method was used to estimate stream temperatures. Nine global climate models simulations for Representative Concentration Pathways RCP4.5 and RCP8.5 scenarios were downscaled to the local level. Significant reductions in streamflow were predicted to occur in all of the basins (max. −49 %) by the year 2099, and seasonal differences were noted between the basins. The stream temperature models showed relationships between the model parameters, geology and hydrologic responses. Temperature was sensitive to streamflow in one set of streams, and summer reductions in streamflow contributed to additional stream temperature increases (max. 3.6 °C), although the sites that are most dependent on deep aquifers will likely resist warming to a greater degree. The predicted increases in water temperatures were as high as 4.0 °C. Temperature and streamflow changes will cause a shift in the rear edge of the distribution of this species. However, geology will affect the extent of this shift. Approaches like the one used herein have proven to be useful in planning the prevention and mitigation of the negative effects of climate change by differentiating areas based on the risk level and viability of fish populations.