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

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.

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