We report development of the Local Climate Model (LCM) that is a statistical downscaling procedure relating a suite of dependent variables (stochastic structure of daily maximum temperature) using canonical correlation analysis to a suite of independent variables (derived from insolation, a DEM and GCM velocity, geopotential heights and SST). Our method differs from the other statistical techniques of downscaling (e.g., Zorita and von Storch, 1999) as it uses a semi-Lagrangian approach to represent the regional climate controls at the point where climate is modeled. The stochastic structure of daily temperature is represented by (1) parameters of the best fit of insolation to maximum daily temperature, (2) parameters of the third order autoregressive (AR3) model fit to the residuals from the insolation model, (3) variance of the residuals of the AR3 model; and, (4) mean and variance of temperature range. The LCM suite of independent variables comprises surface, upwind, and downwind variables computed using CCM1 boundary conditions. Surface variables represent local topography and radiation balance. Upwind variables are: radiation balance air temperature and its meridional gradient, average gradient in saturation vapor pressure, speed, div and curl of velocity as well as orographic controls. Downwind (30 km) variables represent blocking effects. The transfer function was calibrated (using 434 stations in an elevation- and climate-division-stratified sample) and tested (using 1290 stations) for climate of the western U.S. and Mexico using a control run of CCM1 for boundary conditions. This new model allows high temporal (one day) and spatial (1km2) resolution solutions of daily temperature for much of western North America for any time period for which CCM1 solutions are available, e.g. 21 and 6ka paleoclimate. Similar solutions can be used for other GCMs when calibrated with their control runs.