Extreme precipitation shows non-stationary behavior over time, but also with
respect 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 use
annual maxima as well as seasonal maxima to be fitted in a generalized extreme
value setting. To further increase the efficiency of data usage maxima from
different accumulation durations are aggregated so that information for
extremes on different time scales can be provided. Our model is trained to
individual station data with temporal resolutions ranging from one minute to
one day across Germany. The models are selected with a stepwise BIC model
selection and verified with a cross-validated quantile skill index. The
verification shows that the new model performs better than a reference model
without large scale information. Also, the new model enables insights into the
effect of large scale variables on extreme precipitation. Results suggest that
the probability of extreme precipitation increases with time since 1950 in all
seasons. High probabilities of extremes are positively correlated with blocking
situations in summer and with temperature in winter. However, they are
negatively correlated with blocking situations in winter and temperature in
summer
