AbstractA novel stochastic downscaling approach to simulate ensembles of daily precipitation fields using the Gaussian copula is presented. In contrast to many other statistical downscaling techniques, this approach uses spatial correlation (correlograms) to derive the transfer function between predictors and predictands for a parsimonious model structure. Daily regional climate model (RCM) simulations for a region in Central Europe in two different spatial resolutions (7 and 42 km) served as a training set to derive the statistics necessary to simulate fine scale precipitation values. The model was calibrated with RCM simulations for the year 1971, and the evaluation was performed for the period 1972–2000 to emulate the typical problem of limited availability of fine scale data. A comprehensive evaluation of the downscaling approach comprising the spatial correlations and statistical distributions of the simulated precipitation fields and several further performance measures was performed. The distribution of simulated precipitation is in close agreement with values simulated from a distribution function that was fitted to the complete evaluation period. Average Brier skill scores of 0.5 indicate a good performance of reproducing the daily dynamical simulations for most regions. A comparison with precipitation fields interpolated with inverse distance weighting revealed an average added skill of 42% for different precipitation thresholds; 87% of the dry days and 71% of the wet days were simulated correctly. An advantage of the proposed method over deterministic downscaling techniques is that ensembles of predictand fields are generated. Thus, the uncertainty that is inherent to downscaling can be estimated. The method has the potential to be used in other downscaling applications to generate ensembles of spatially correlated predictands based on other predictors. As copulas treat the dependence structure separately from the marginal distributions of the predictors and predictands, it is possible to simulate meteorological variables from any desired distribution function.