Accurate heavy precipitation prediction in an (pre-)alpine area: The benefit of trend conservation in circulation pattern conditional statistical downscaling.

Abstract

The reliable prediction of flash flood relevant heavy precipitation events under climate change conditions remains a challenging task for the downscaling community. Therefore, a huge variety of downscaling approaches have been proposed and successfully applied, however, there is still potential for improvements. The conducted study aims to investigate potential improvements by circulation pattern (CP) trends conservation and their utilization for CP conditional statistical downscaling of daily summer precipitation in the (pre-)alpine region of Bavaria. The CPs have been created taking only atmospheric variables into consideration and the link to precipitation is established via CP conditional cumulative distribution functions (CDF) of the observed precipitation at selected measurement sites across the region. The derived CDFs allow for the sampling of CP conditional precipitation values at the station scale which are subsequently bias corrected by quantile mapping (QM) and parametric transfer functions (PTFs) as tested methods. The predicted precipitation values have been evaluated against obervations using different performance measures such as Kling-Gupta Efficiency (KGE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). In order to properly account for extreme events the evaluation has been conducted for the complete precipitation distribution and for the distribution above the 95th percentile seperately. The results show that the described CP conditional downscaling approach is capable of yielding more accurate daily precipitation values especially in the extremes compartment in which an average gain in prediction skill of + 0.24 and a maximum gain of + 0.6 in terms of KGE has been observed. This shows that the conservation of trends and atmospheric information through CPs and their utilization for downscaling can lead to improved precipitation downscaling results.