Evaluating skills and issues of quantile-based bias adjustment for climate change scenarios

Abstract

Daily meteorological data such as temperature or precipitation from climate models are needed for many climate impact studies, e.g., in hydrology or agriculture, but direct model output can contain large systematic errors. A large variety of methods exist to adjust the bias of climate model outputs. Here we review existing statistical bias-adjustment methods and their shortcomings, and compare quantile mapping (QM), scaled distribution mapping (SDM), quantile delta mapping (QDM) and an empiric version of PresRAT (PresRATe). We then test these methods using real and artificially created daily temperature and precipitation data for Austria. We compare the performance in terms of the following demands: (1) the model data should match the climatological means of the observational data in the historical period; (2) the long-term climatological trends of means (climate change signal), either defined as difference or as ratio, should not be altered during bias adjustment; and (3) even models with too few wet days (precipitation above 0.1 mm) should be corrected accurately, so that the wet day frequency is conserved. QDM and PresRATe combined fulfill all three demands. For (2) for precipitation, PresRATe already includes an additional correction that assures that the climate change signal is conserved.