Climate change studies are crucial to assist decision-makers in understanding future risks and planning adequate adaptation measures. In general, Global/Regional Climate Models (GCMs/RCMs) achieve coarse resolutions, and are thus unable to provide sufficient information to conduct local climate assessments. Downscaling, defined as a method that derives local to regional-scale (10 to 100 km) information from larger-scale models or data analyses, is used to address this deficiency. In this thesis, a particular downscaling technique, known as the Quantile-Quantile transformation, was used to adjust the statistical distribution of RCM variables to match the statistical distribution of the observed variables generated by two RCMs: the Canadian Regional Climate Model version 3.7.1 and the ARPEGE model, on the historical period (1961-2001). The analyses presented in this study were applied to daily precipitation and maximum and minimum temperatures in the South Nation watershed in Eastern Ontario, Canada. The two-sample Kolmogorov–Smirnov test indicated that the Quantile-Quantile transformation improved the shape of the PDF of RCM-simulated climate variables. The results suggest that, under the A1B scenario, temperatures in the watershed would rise significantly and there would be an increment in precipitation occurrence and intensity. Trend analysis was performed on the 1961 to 2001 and 2041 to 2081 timeframes, using the Mann-Kendall test and the Sen's slope estimator. Discernible, often significant, increases of maximum and minimum temperatures were found for the 1961 to 2001 period, and stronger ascending slopes for the 2041 to 2081 period. However, there was marginal evidence of changes in the time series of maximum and accumulated annual precipitation for both periods. The study also outlined how the frequency and intensity of some extreme weather events will evolve in the 2041-2081 period in response to the rise in atmospheric GHG concentrations. Projected impacts were investigated by tracking future changes in four extreme temperature indices and three precipitation indices. It was predicted that heavy precipitation events and warm spells will occur more frequently and intensely, while extreme cold events will be weaker, and some will be hardly observed.