Although RCMs have already proven their capability to simulate regional climate and its variability, they still feature systematic errors compared to observations. Besides their steady enhancement, empirical-statistical post-processing, based on the concept of model output statistics (MOS), provides a ready opportunity to mitigate RCM error characteristics and to further downscale climate model data to the point-scale. In the course of this PhD work, seven empirical-statistical downscaling and error correction methods (DECMs) are inter-compared for their applicability to and error correction potential for daily precipitation, temperature, and derived extreme indices from RCMs in Europe. Furthermore, error corrected climate scenarios for the respective parameters are generated for Europe and the impact of DECMs on the climate change signal (CCS) is investigated. Overall, the findings of this PhD work strongly emphasize the combination of RCMs and DECMs to provide suitable climate data for climate impact assessments and decision making. DECMs drastically reduced the error characteristics of RCMs regarding mean, variability, and extremes. Particularly, Quantile Mapping (QM) resulted in outstanding error correction potential and can be considered as highly recommendable due to its simplicity and flexibility. In application to future climate scenarios QM only moderately modified the CCSs of mean, minimum, maximum temperature, and precipitation amount. In contrast, QM strongly changed the CCSs of non-linearly derived indices of extremes such as threshold indices in some cases. However, these modifications were considered as reasonable because the respective uncorrected parameters featured magnitude-dependent error characteristics and trends in the future scenarios.Besides, this PhD work also defined useful climate data from the point of view of the climate impact community and decision makers in order to promote collaborations with the climate modeling community.