The Statistical Downscaling Model (SDSM) is a decision support tool, developed by Drs. Robert Wilby and Christian Dawson in the UK, for assessing local climate change impacts using a robust statistical downscaling technique. It is a hybrid of a stochastic weather generator and regression-based downscaling methods and facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future climate forcing. SDSM is designed to help the user identify those large-scale climate variables (the predictors) which explain most of the variability in the climate (the predictand) at a particular site and statistical models are then built based on this information. Statistical models are built using daily observed data – local climate data for a specific location for the predictand and larger-scale NCEP data for the predictors – and these models are then used with GCM-derived predictors to obtain daily weather data at the site in question for a future time period.