An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment

Abstract

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.

A Statistical Parameter Correction Technique for WRF Medium-Range Prediction of Near-Surface Temperature and Wind Speed Using Generalized Linear Model

“…The GLM-based bias correction is similar to the quantile matching (QM) algorithm [26,27], as the cumulative probabilities from the uncorrected model are used in the corrected distribution. The difference is that the bias correction used in this study assumed theoretical distribution for the daily distribution, while QM considered the empirical distribution for the entire training period.…”

“…The aim of this study was to correct the entire inland area of South Korea. Therefore, we assumed the area as one block [27], and evaluated the performance between QM and the bias correction that we developed.…”

“…Therefore, we assumed the area as one block [27], and evaluated the performance between QM and the bias correction that we developed. The general problem of bias correction in this and similar studies is referred to as the "Mars problem" by Maraun and Widmann [28].…”

Comparison of statistical downscaling methods with respect to extreme events over Europe: Validation results from the perfect predictor experiment of the COST Action VALUE

“…The selected indices for extremes of temperature and precipitation which quantify the aspects as well as the corresponding performance measures are given in Table . The details of the calculation of the indices can be found on the validation portal under http://www.value-cost.eu/validationportal and are also described in Gutiérrez et al (). In addition to the bias between prediction and observation as performance measure, we used the Brier skill score (BSS) for the validation of dichotomous events and the (censored) quantile verification skill score ((C)QVSS) to estimate the relative gain of the (C)QR‐predictions over a reference forecast taken from the climatological distribution.…”

“…Section 2 describes the data and methods used. Details on the data can also be found in Kotlarski et al (), on the downscaling methods, predictor variables, and the validation setup in Gutiérrez et al (). Section 3 presents the results of the validation of extremes.…”