The characteristics of precipitation in regional climate model simulations deviate considerably from those of the observed data; therefore, bias correction is a standard part of most climate change impact assessment studies. The standard approach is that the corrections are calibrated and applied separately for individual spatial points and meteorological variables. For this reason, the correlation and covariance structures of the observed and corrected data differ, although the individual observed and corrected data sets correspond well in their statistical indicators. This inconsistency may affect impact studies using corrected simulations.This study presents a new approach to the bias correction utilizing principal components in combination with quantile mapping, which allows for the correction of multivariate data sets. The proposed procedure significantly reduces the bias in covariance and correlation structures, as well as that in the distribution of individual variables. This is in contrast to standard quantile mapping, which only corrects the individual distributions, and leaves the dependence structure biased.

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