Open Access

The overall aim of this PhD-thesis is to develop empirical probabilistic frameworks that help to quantify the impacts of temporal and spatial scale dependencies and model uncertainties of climate projections regarding precipitation-dependent parameters. The thesis is structured in four journal articles. Article one is the first study that analyzed climate projections from the spatially highly resolved regional climate model (RCM) ensemble EURO-CORDEX. Additionally, the significance and the robustness of the projected changes are analyzed, and improvements related to the higher horizontal resolution of the new data set are discussed. A major finding is, that RCM simulations provide higher daily precipitation intensities, which are missing in the global climate model (GCM) simulations, and that they show a significantly different climate change of daily precipitation intensities with a smoother shift from low towards high intensities. The second article elaborates on impacts of temporal and spatial aggregation on extreme precipitation intensities. By combining radar data with cloud observations, the different temporal and spatial scaling behavior of stratiform and convective type precipitation events can be analyzed for the first time. The separation between convective and stratiform type events also allows to quantify the contribution of convective events to the extremes. Further, it is shown that temporal averaging has similar effects on the precipitation distribution as spatial averaging. Associated pairs of temporal and spatial resolutions that show comparable intensity distributions are identified. Using precipitation data from radar observations, a gauge station network and a spatially highly resolved regional climate model, the third paper optimizes the process that finds associated temporal and spatial scales (see second article). This information is used to develop a method that adjusts point measurements to the temporal and spatial scale of a previously defined model grid. The study shows that this procedure can be used to improve bias-adjustment methods in areas with a low gauge station density. It is known that the EURO-CORDEX ensemble overestimates precipitation and shows a common cold bias in the Alpine region. The fourth article evaluates how these biases are changing the temperature distribution and the temperature dependency of precipitation-frequencies. These biases are a source of uncertainty that is not captured by the robustness tests performed in the first article. A probabilistic-decomposition-framework is developed to quantify the impact of these biases on precipitation-frequency changes and to investigate causes for the ensemble spread.