| ABSTRACT | The average temperature at the surface of the Earth has been increasing over the past century due to the increased greenhouse gas concentrations in atmosphere, intensifying the components of water cycle which results in drought and flood conditions in different parts of the world through changes in globally averaged precipitation. To make a stand for these rapid and extreme changes, Global Circulation Models (GCM) and Regional Climate Models (RCM) are used to model present climate and to predict the future climate. However, because of running at coarse spatial resolution, GCMs are not often sufficient to represent the variability in climate variables especially for the regions of complex topography, coastal or island locations and heterogeneous land cover etc, thus their results cannot be directly used in hydrological models for climate change impact studies at local scale. Therefore, it is needed to find a way to downscale the climate variables obtained from GCMs/RCMs in order to have finer grid scale before using them in a hydrologic model. This study examines the performance of two different statistical downscaling methods, i.e. “change factor” (also known as delta change method) and “bias correction of mean” method in representing the catchment characteristics. Both methods are applied to a list of GCM/RCM outputs (i.e. precipitation and temperature). The downscaled precipitation and temperature data are used in a hydrologic model (HBV model) configured for Omerli Basin to assess the climate change impact on surface runoff. Finally, uncertainty related to model calibration with respect to GCM/RCM uncertainties is also shown with Nash-Sutcliffe Efficiency (NSE) evaluations. |