Extreme precipitation in the past and future

We create Intensity-Duration-Frequency (IDF) curves which describe the major statistical characteristics of extreme precipitation events (return level, return period, time scale) and are capable of considering large-scale influences. They provide information on the probability of exceedance of certain precipitation intensities for a range of durations from minutes to days and can help to visualize either how extreme (in terms of probability/frequency/return period) a specific event is or which intensity is expected for a given probability. We modeled the underlying distribution of block maxima with the Generalized Extreme Value (GEV) distribution. Maxima from different durations are used and enable a model that can evaluate different time scales. All durations are modeled in one single model in order to prevent quantile-crossing and to assure that estimated quantiles are consistent.The influence of climate change is included by letting the GEV parameters (covariates) depend on the variables NAO, temperature, humidity, blocking and year (as a proxy for climate change). We found an increase in probability of extreme precipitation with year and temperature, while the effect of the other variables depends on the season. This corresponds to the aim of the sessionUP3.1, to answer whether there have been significant changes in frequency or amplitude of extreme events. Since it is easier to project average values than to project extremes, we use the modeled relations between average large-scale covariates and extreme precipitation to create future IDF-relations based on climate projections and the projected average large-scale values. This poses some challenges because the polynomial dependencies of the past might not hold for an extrapolation into the future. Furthermore, we plan to add a spatial component to the model that enables the usage of data from several neighboring stations in one model and interpolate to ungauged sites. This will be the basis for investigating how gridded data sets can be used to complement the station-based approach. One focus will lie on the dependence between neighboring grid points.