Abstract. In the current study two regional climate models (MM5 and REMO) driven by different global boundary conditions (ERA reanalysis and the ECHAM5 model) are coupled with the uncalibrated hydrological process model PROMET in order to analyze the impact of global boundary conditions, dynamical regionalization and subsequent statistical downscaling (bilinear interpolation, correction of subgrid-scale variability and combined correction of subgrid-scale variability and bias) on river runoff simulation. The results of 12 coupled model runs set up for the catchment of the Upper Danube over the historical period 1971–2000 indicate that the correction of subgrid-scale variability compared to a bilinear interpolation allows for a more accurate simulation of discharge in case of all model configurations and all discharge criteria considered (mean monthly discharge, mean monthly low-flow discharge and mean monthly peak-flow discharge). Further improvements in the hydrological simulations could be achieved by eliminating the biases (in terms of deviations from observed meteorological conditions) inherent in the driving RCM simulations, regardless of the global boundary conditions or RCM applied. Comparing the hydrological results achievable with MM5 and REMO, the application of bias corrected MM5 simulations turned out to allow for a more accurate simulation of discharge volumes while the variance in simulated discharge was often better reflected in case of REMO forcings. The results achieved with different global boundary conditions are characterized by only minor differences. It is, however, noteworthy that all efficiency criteria in case of bias corrected MM5 simulations indicate better performance under ERA40 boundaries, whereas REMO-driven hydrological simulations better correspond to measured discharge under ECHAM5 boundaries. In spite of all downscaling and bias correction efforts described, the RCM-driven hydrological simulations remain less accurate than those achievable with spatially distributed meteorological observations.