Long-term, high-resolution data about hydrologic fluxes and states are needed for many hydrological applications. Because continuous large-scale observations of such variables are not feasible, hydrologic or land surface models are applied to derive them. This study aims to analyze and provide a consistent high-resolution dataset of land surface variables over Germany, accounting for uncertainties caused by equifinal model parameters. The mesoscale Hydrological Model(mHM)isemployedtoderiveanensemble(100members)ofevapotranspiration,groundwaterrecharge,soilmoisture, and runoff generated at high spatial and temporal resolutions (4km and daily, respectively) for the period 1951– 2010. The model is cross-evaluated against the observed dailystreamflowin222basins,whicharenotusedformodel calibration. The mean (standard deviation) of the ensemble median Nash–Sutcliffe efficiency estimated for these basins is0.68(0.09)fordailystreamflowsimulations.Themodeled evapotranspirationandsoilmoisturereasonablyrepresentthe observations from eddy covariance stations. Our analysis indicates the lowest parametric uncertainty for evapotranspiration, and the largest is observed for groundwater recharge. The uncertainty of the hydrologic variables varies over the course of a year, with the exception of evapotranspiration, which remains almost constant. This study emphasizes the role of accounting for the parametric uncertainty in modelderived hydrological datasets.