Added value of an atmospheric circulation pattern‐based statistical downscaling approach for daily precipitation distributions in complex terrain

Reliable prediction of heavy precipitation events causing floods in a world changing climate is crucial for the development appropriate adaption strategies. Many attempts to provide such predictions have already been conducted but there still much potential improvement left. This particularly true statistical downscaling due changes present corresponding atmospheric drivers. In this study, circulation pattern (CP) conditional station level proposed which considers occurring frequency CPs. Following strict circulation-to-environment approach we use predictors derive Subsequently, observations are used CP cumulative distribution functions (CDFs) daily precipitation. Raw time series sampled from these CDFs. Bias correction applied with quantile mapping (QM) and parametric transfer (PTFs) as methods being tested. The added value evaluated against common non-CP approach. performance evaluation by using Kling–Gupta Efficiency (KGE), root mean squared error (RMSE), absolute (MAE) metrics. both cases bias identical. Potential can therefore only be attributed conditioning. It shown that capable yielding more reliable accurate downscaled comparison seen particular extreme parts distribution. Above 95th percentile, an average gain +0.24 maximum +0.6 terms KGE observed. These findings support assumption conserving utilizing information through CPs beneficial downscaling. Due availability model output, presented method potentially suitable projections.