Evaluating the effect of the statistical downscaling method on monthly precipitation estimates of global climate models

Researches to foresee the possible effects of climate change on the environment and living beings for taking necessary precautions on time have increased in recent years. In the improvement of these studies, especially the reduction of estimation errors by downscaling the outputs of global climate models played an important role. In this study, a model that can predict monthly precipitation amounts in the future by using downscaling methods in different global climate models were applied in Antakya district of Hatay province, and the model results were evaluated. The predictive parameters for global climate models were determined using downscaling methods by applying correlation analysis for the study area. As a result of this analysis, it was seen that the air temperature and specific humidity values at the pressure level of 925 hPa and the geopotential height value at the 300 hPa pressure level had the best correlation for the years 1970-2005. The usability of three different global climate models (CanESM2, GISS-E2H, and CSIRO Mk 3-6-0) for the forecast of future rainfall in the Antakya district of Hatay province was investigated using multiple linear regression analysis, one of the downscaling methods. As a result of the statistical analysis, it was seen that the use of the downscaling method increased the accuracy of all prediction models.