Nacar, SinanOkkan, Umut2024-10-312024-10-312022-04-010177-7971https://doi.org/10.1007/s00703-022-00878-6https://hdl.handle.net/11452/47232Climate community frequently uses gridded reanalysis data sets in their climate change impact studies. However, these studies for a region yield more realistic results depending on the rigorous analysis of the reanalysis data sets for this region. This study aims to determine the most suitable reanalysis data set for the statistical downscaling method in the Eastern Black Sea Basin, one of Turkey's most important hydrological basins owing to the precipitation it receives throughout the year. For this purpose, the monthly mean temperature and total precipitation data measured from the 12 meteorological stations and 12 large-scale predictors of the NCEP/NCAR, ERA-Interim, and ERA5 reanalysis data sets were used. The multivariate adaptive regression splines (MARS) and conventional regression analysis with linear and exponential functions were used to create effective statistical downscaling models. For evaluating and comparing the performance of the downscaling models with three different reanalysis data set, four performance statistics (root means square error, scatter index, mean absolute error, and the Nash Sutcliffe coefficient of efficiency) were used. Besides, the relative importance of the input variables of the models was determined. The study revealed that the values obtained from the models of ERA5 were closer to the precipitation and temperature values measured from the meteorological stations. In addition, the model performances with three reanalysis data sets for the temperature variable were very close to each other. The study results have shown that the MARS method, which gives the highest performance values, can be used successfully as a statistical downscaling method in climate change impact studies.eninfo:eu-repo/semantics/closedAccessAdaptive regression splineLearning based optimizationClimate-change scenariosSupport vectorRegional climateTime-seriesPrecipitationModelTemperatureCirculationScience & technologyPhysical sciencesMeteorology & atmospheric sciencesArticle000770314900001134210.1007/s00703-022-00878-6