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In many climate impact studies hydrological models are forced with meteorological data without an attempt to assess the quality of these data. The objective of this study was to compare downscaled ERA15 (ECMWF-reanalysis data) precipitation and temperature with observed precipitation and temperature and apply a bias correction to these forcing variables. Precipitation is corrected by fitting it to the mean and coefficient of variation (CV) of the observations. Temperature is corrected by fitting it to the mean and standard deviation of the observations. It appears that the uncorrected ERA15 is too warm and too wet for most of the Rhine basin. The bias correction leads to satisfactory results, precipitation and temperature differences decreased significantly, although there are a few years for which the correction of precipitation is less satisfying. Corrections were largest during summer for both precipitation and temperature. For precipitation alone large corrections were applied during September and October as well. Besides the statistics the correction method was intended to correct for, it is also found to improve the correlations for the fraction of wet days and lag-1 autocorrelations between ERA15 and the observations. For the validation period temperature is corrected very well, but for precipitation the RMSE of the daily difference between modeled and observed precipitation has increased for the corrected situation. When taking random years for calibration, and the remaining years for validation, the spread in the mean bias error (MBE) becomes larger for the corrected precipitation during validation, but the overal average MBE has decreased

Evaluation of a bias correction method applied to downscaled precipitation and temperature reanalysis data for the Rhine basin