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Extreme precipitation shows non-stationary behavior over time, but also withrespect to other large-scale variables. While this effect is often neglected,we propose a model including the influence of North Atlantic Oscillation, time,surface temperature and a blocking index. The model features flexibility to useannual maxima as well as seasonal maxima to be fitted in a generalized extremevalue setting. To further increase the efficiency of data usage maxima fromdifferent accumulation durations are aggregated so that information forextremes on different time scales can be provided. Our model is trained toindividual station data with temporal resolutions ranging from one minute toone day across Germany. The models are selected with a stepwise BIC modelselection and verified with a cross-validated quantile skill index. Theverification shows that the new model performs better than a reference modelwithout large scale information. Also, the new model enables insights into theeffect of large scale variables on extreme precipitation. Results suggest thatthe probability of extreme precipitation increases with time since 1950 in allseasons. High probabilities of extremes are positively correlated with blockingsituations in summer and with temperature in winter. However, they arenegatively correlated with blocking situations in winter and temperature insummer.

Non-Stationary Large-Scale Statistics of Precipitation Extremes in Central Europe