| Summary: | Description These data were used in the study "Non-Stationary Large-Scale Statistics of Precipitation Extremes in Central Europe" (Fauer et al., 2022). Rainfall data were collected from stations by the German Meteorological Service (DWD) and Wupperverband (corrected data). Raw time series data from the German Meteorological Service is publicly available under https://opendata.dwd.de/climate_environment/CDC/observations_germany/climate/. Only the annual and monthly precipitation maxima over different durations are published here. For more detailed information on our work and the modeling of extreme rainfall data, see also Fauer et al. (2021, https://doi.org/10.5194/hess-25-6479-2021). Files precipMax_and_covariates.csv : This file contains aggregated rainfall data over different durations and for different stations. Also, it contains covariates for the variables "blocking", "NAO" and surface air temperature ("tas") and their polynomials up the the fourth order. precip_meta.csv: This file contains additional information of the different stations such as longitude, latitude, altitude, temporal resolution (m=minutely, h=hourly, d=daily), group. The same group is assigned to stations which have a distance of less than 250 meters and can be treated as one station. The value in "group" corresponds to the value "station" in precipMax_and_covariates.csv. Abstract of the according study 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 ...
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