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Much of our knowledge about future changes in precipitation relies onglobal (GCMs) and/or regional climate models (RCMs) that have resolutionswhich are much coarser than typical spatial scales of precipitation,particularly extremes. The major problems with these projections are bothclimate model biases and the gap between gridbox and point scale.Wong et al. (2014) developed a model to jointly biascorrect and downscale precipitation at daily scales. This approach, however,relied on pairwise correspondence between predictor and predictand forcalibration, and, thus, on nudged simulations which are rarely available.Here we present an extension of this approach that separates the downscalingfrom the bias correction and in principle is applicable to free-runningGCMs/RCMs. In a first step, we bias correct RCM-simulated precipitationagainst gridded observations at the same scale using a parametric quantilemapping (QMgrid) approach. In a second step, we bridge the scalegap: we predict local variance employing a regression-based model withcoarse-scale precipitation as a predictor. The regression model is calibratedbetween gridded and point-scale (station) observations. For this concept wepresent one specific implementation, although the optimal model may differfor each studied location. To correct the whole distribution includingextreme tails we apply a mixture distribution of a gamma distribution for theprecipitation mass and a generalized Pareto distribution for the extreme tailin the first step. For the second step a vector generalized linear gammamodel is employed. For evaluation we adopt the perfect predictor experimentalsetup of VALUE. We also compare our method to the classical QM as it isusually applied, i.e., between RCM and point scale (QMpoint).Precipitation is in most cases improved by (parts of) our method acrossdifferent European climates. The method generally performs better in summerthan in winter and in winter best in the Mediterranean region, with a mildwinter climate, and worst for continental winter climate in Mid- andeastern Europe or Scandinavia. While QMpoint performs similarly(better for continental winter) to our combined method in reducing the biasand representing heavy precipitation, it is not capable of correctly modelingpoint-scale spatial dependence of summer precipitation. A strength of thistwo-step method is that the best combination of bias correction anddownscaling methods can be selected. This implies that the concept can beextended to a wide range of method combinations

A combined statistical bias correction and stochastic downscaling method for precipitation