Abstract
Non‐stationarities within the predictor–predictand relationships can substantially affect model skill of statistical downscaling approaches. For this reason, an approach is presented which takes varying predictor–predictand relationships explicitly into account. Seasonal aggregated daily precipitation extremes (90th, 95th and 99th quantiles) were assessed by means of 31‐year running contiguous calibration periods. Here, when the end of the time series was reached years from the beginning were attached. Thus, a regression model ensemble (RME) becomes available, with the number of established statistical models corresponding to the number of years within the time series. With respect to non‐stationarities in the projection periods, a novel approach is presented. A single model of the RME is determined for each sub‐period which seems to be the most suitable model (MSM) for assessing precipitation extremes within the respective period. In order to define the MSM weighted correlations of atmospheric circulation composites, represented by the geopotential heights, between reanalysis and model runs were calculated for all significant predictors. The reanalysis period which exhibits the highest mean correlation coefficient is then considered for the projections of the respective period of the model runs. Subsequently, a second selection process was performed in order to determine whether the regression model (RM) established within this period or the model with the highest skill within this period is used for assessing future precipitation extremes. Results have shown that projections can significantly differ depending on whether a non‐stationary or a stationary downscaling setup is used for the assessments.
Published Version
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