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The use of a statistical downscaling technique is needed to investigate the hydrological consequences of climate change on the local hydropower capacity. Global Circulation Models (GCMs) are crucial tools used in various simulations for potential climate change effects, including precipitation and temperature. Statistical downscaling methods comprise the improvement of relations between the large-scale climatic parameters and the local variables. This study presents the trend analysis of the observed variables compared to the statistically downscaled emission scenarios that are adopted from the Canadian Second Generation Earth Systems Model (CanESM2) in the basin of Göksu River which is located in Turkey. The key purpose of the research is to evaluate both the predicted monthly precipitation and the projections of GCMs within the three simulated scenarios of RCP2.6, RCP4.5, and RCP8.5 by Gene Expression Programming (GEP). In addition, the findings of statistical downscaling of monthly mean precipitation will be compared to the Linear Regression (LR) model. The R-value is 0.827 and 0.755 for precipitation of the GEP model for the periods of calibrating and validation. In comparison with the LR model for the validation and calibration periods (1971–2005), the results of the GEP model prove its applicability in projecting the data for monthly mean rainfall. Generally, in the simulated periods of 2021–2100, the mentioned scenarios forecast a decline in the monthly mean precipitation in the basin. Moreover, the scenario of RCP8.5 projected more suitably for the case study than expected under the scenarios RCP4.5 and RCP2.6. The mean statistically downscaled CanESM2 model was compared with the trend analysis of the areal mean precipitation over the case study area, and the trend was shown as decreasing. However, the RCP 8.5 scenario was the more quasi-asymptotic for trend.

Investigation of impact of climate change on small catchments using different climate models and statistical approaches