Empirical-statistical downscaling and error correction of daily precipitation from regional climate models

摘要

Although regional climate models (RCMs) are powerful tools for describing regional and even smaller scale climate conditions, they still feature severe systematic errors. In order to provide optimized climate scenarios for climate change impact research, this study merges linear and nonlinear empirical-statistical downscaling techniques with bias correction methods and investigates their ability for reducing RCM error characteristics. An ensemble of seven empirical-statistical downscaling and error correction methods (DECMs) is applied to post-process daily precipitation sums of a high-resolution regional climate hindcast simulation over the Alpine region, their error characteristics are analysed and compared to the raw RCM results. Drastic reductions in error characteristics due to application of DECMs are demonstrated. Direct point-wise methods like quantile mapping and local intensity scaling as well as indirect spatial methods as nonlinear analogue methods yield systematic improvements in median, variance, frequency, intensity and extremes of daily precipitation. Multiple linear regression methods, even if optimized by predictor selection, transformation and randomization, exhibit significant shortcomings for modelling daily precipitation due to their linear framework. Comparing the well-performing methods to each other, quantile mapping shows the best performance, particularly at high quantiles, which is advantageous for applications related to extreme precipitation events. The improvements are obtained regardless of season and region, which indicates the potential transferability of these methods to other regions.