This thesis presents a new multivariate statistical approach to downscale the hydroclimatic variables associated with global climate signals, from low-resolution Global Climate Models (GCMs) to high-resolution grids that are appropriate for regional and local hydrologic analysis in west and northwestern Iran. The approach uses Principal Component Analysis (PCA) and Multichannel Singular Spectrum Analysis (MSSA) to: evaluate significant variation modes among global climate signals and spatially distributed hydroclimatic variables within certain spatial domai downscale the GCMs’ projections of the hydroclimatic variables using these significant modes of variation and using the results to evaluate the rainfed wheat yield in the study area by the Decision Support System for Agrotechnology Transfer (DSSAT) model. Key Words: Climate Change, Principal Component Analysis, Multichannel Singular Spectrum Analysis, Spatio Temporal Variation, Iran, Rainfed Wheat, DSSAT model Introduction Having consistent information about the likelihood of drought occurrence in semi-arid regions, like west and northwestern Iran, is one of the aims of hydrologic assessment for
