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Abstract

Evaluating how the probability of extreme precipitation events changes with respect to climate change can help preventing casualties and reducing impact consequences. We create Intensity-Duration-Frequency (IDF) curves which describe the major statistical characteristics of extreme precipitation events (return level, return period, time scale). They provide information on the probability of exceedance of precipitation intensities and help to visualize how extreme the event for different durations is. We modeled the underlying distribution with the Generalized Extreme Value (GEV) distribution. The scarce availability of data can be addressed by using the available data more efficiently. Therefore, including maxima from different measurement durations is useful for (1) gathering more information from the data and (2) estimating return periods for different time scales with a consistent modeling approach. Duration-dependence is implemented in a consistent setting. To include large-scale information, each of the GEV parameters was modeled with linear dependence on the large-scale variables temperature, blocking situation, humidity, year and North Atlantic oscillation (NAO), all spatially and monthly averaged. We show that the probability of extreme events increases with time, temperature and humidity over all seasons (summer, winter, whole year). The effects of blocking situation and NAO depend on the season with positive NAO leading to stronger events only in winter and blocking leading to stronger events only in summer and vice versa. A cross-validated model verification shows improvement over a reference model without large-scale information. This study is conducted on precipitation data from ~200 stations across Germany with temporal measurement resolutions from minutes to days.