Abstract: Kazakhstan is the largest landlocked country in the world with a typical continental climate, and its natural environment and human society are sensitive and vulnerable to climate change. In climate change impact studies, one widely-used approach to obtain future climate change scenario at regional or local scale is to downscale future climate projection from General Circulation Model (GCM). So far, to our knowledge, no statistical downscaling study has been carried out in Kazakhstan region. In this study, the authors explored and validated the ability of a statistical downscaling model that is based on ridge regression to predict monthly mean temperatureat of 11 stations in Kazakhstan from NCEP/ NCAR monthly mean reanalysis. The 30-year dataset for the period from 1960 to 1989 was used to train the downscaling model and the next 20-year data for the period of 1990-2009 was used for validation of the downscaling model. The result shows that despite certain disagreements with observations at several stations, the ridge regression model generally is able to reasonably reproduce monthly mean temperature over Kazakhstan region. The authors also find that the performance of the ridge regression model is better in the summer than in the winter and better in flat terrain areas than in complex terrain areas.