Suitability of CMIP6 Models Considering Statistical Downscaling Based on GloH2O and E-OBS Dataset in River Basin Districts of the Southeastern Baltic Sea Basin

Abstract

Climate projections based on global climate models (GCMs) are generally subject to large uncertainties, as the models only reflect the local climate in the past to a limited extent. Statistical downscaling is the most cost-effective approach to identify the systematic biases of the GCMs from the past and eliminate them in the projections. This study seeks to evaluate the effectiveness of GCMs in capturing local climatic characteristics at the river basin district scale by applying gridded statistical downscaling techniques using global and regional datasets. The historical observational datasets E-OBS and GloH2O were selected to downscale the raw data of 17 GCMs from ~1° grid cells to 0.25° resolution. E-OBS is a regional dataset supported by a dense network of meteorological stations in Europe, while GloH2O is a global dataset covering all continents. The results show that the suitability of the GCMs varies depending on the selected parameter. The statistical downscaling revealed the advantages of the performance of E-OBS in representing local climate characteristics during the historical period and emphasized the crucial role of regional datasets for good climate depiction. Such an approach provides the possibility to assess the relative performance of GCMs based on the high-resolution observational and reanalysis datasets, while generating statistically downscaled datasets for the best ranked GCMs. The strategies used in this study can help to identify the most appropriate models to assemble the right ensemble of GCMs for specific studies.