The robustness of hydrological models used in projections of future fresh water resources is compromised due to non-stationary climate conditions. This study aims to (i) develop a method for selecting a skillful hydrological model parameterization under changing climate conditions and (ii) apply a calibrated hydrological model to assess streamflow projections for 38 mountain watersheds in the eastern Mediterranean island of Cyprus over the next decades (2030–2060). A matrix-based approach was developed to evaluate six objective functions by eight performance measures. Using the GR4J hydrological model, evaluation matrices were computed for multiple 5-year simulation runs covering 1980–2015. The matrices covered 14 model calibrations and 182 validations in total, as well as 4 sets of validations under different climate change conditions, for each watershed. Based on the matrix method, the Nash-Sutcliffe Efficiency with square-root transformed streamflow resulted in the best performance for streamflow simulations in Mediterranean watersheds experiencing drying trends. This method is transferable and can be applied in different climate regions to identify the most suitable objective function and model parameterization for hydrologic climate impact assessments. Eighteen Regional Climate Models (RCMs) were bias-corrected, downscaled to 1 km and used to simulate streamflow with GR4J for 1980–2010. Nine RCMs underestimated the fraction of wet period precipitation (60–73 % instead of 82 % of annual precipitation), causing streamflow biases up to 40 %. The remaining nine RCMs selected for the study simulated the seasonal precipitation cycle accurately. The median of future projections showed a 6 % reduction in precipitation and a 17 % reduction in streamflow. In the worst case, reductions could reach 16 % and 39 %, respectively. Notably, during the driest years, streamflow reductions could reach 70 % relative to the driest years in the past. Our findings suggest that terrestrial water resources in the eastern Mediterranean may significantly deteriorate in the coming decades.