Özet:

Climate change caused by anthropogenic causes is one of the biggest global environmental problems of our time. Global climate models and climate change projections have been created to combat climate change on a global scale. Although the outputs of this model are sufficient on a global and continental scale, they are insufficient for measures to be taken against climate change on a local scale. In this study, statistical downscaling of the GFDL-ESM2M global climate model dataset with the Climate Change Toolkit (CCT) software was applied to create change projections of precipitation, minimum temperature and maximum temperature parameters for RCP scenarios. Inverse distance weighted interpolation method was applied to the average change rates obtained, and possible changes in Sivas province were evaluated by creating spatial distribution maps for the near future (2022-2050), mid-future period (2050-2075) and far future (2075-2100). As a result of the study, it has been predicted that the province will experience maximum temperature increases of 8.5 - 37%, minimum temperature increases of 10-171% and a decrease of precipitation of 0 - 26% in different scenarios.

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