This study suggested strategies to project future precipitation series based on a multi-site hybrid SDM (statistical downscaling model), which can downscale precipitation series at multiple observation sites simultaneously by combining the multivariate multiple linear regression (MMLR) model and the stochastic randomization procedure. The hybrid SDM and future projection methodologies applied to 10 observation sites located in the great area of Montréal, Québec, Canada. Six future independent precipitation series were projected from six sets of future atmospheric predictors using three AOGCMs (Atmosphere-Ocean Global Climate Models, i.e. CGCM2, CGCM3, HadCM3) and three IPCC SRES emission scenarios (B2, A1B and A2). Downscaled climate change signals on wet/dry sequences and extreme indices of precipitation time series were evaluated over the future period from 2060 to 2099 with respect to the historical period from 1961 to 2000. The future scenarios of all three AOGCMs showed a consistent increase of 7.9–44.6% in winter while only those of HadCM3 and CGCM3 showed a decrease of 2.3–23.0% in summer compared to their historical values. Precipitation series of CGCM2 A2 and CGCM3 A2 scenarios yielded the largest increase in winter, while those of HadCM3 B2 and A2 scenarios yielded the largest decrease in summer for all statistics indices.