Large gridded climate datasets facilitate correlation with landscape patterns (e.g. with species distribution), but point-based ecological data are often disconnected from the spatial extent and resolution of available climate data. The Global Regression Ecoregional Analysis of Temperature (GREAT) model was developed to describe monthly temperature experienced by locations along elevation and latitudinal gradients in the central Appalachians. Model performance was assessed at 30 m and 1 km spatial resolutions, with subsequent analysis of the tradeoffs between extracting temperatures from an a priori spatial grid versus modeling temperature for specific locations. Results indicate that temperatures modeled at coarser resolution (1 km) had higher correlation with observed station temperatures because models developed at finer spatial scales (30 m) over-emphasize the influence of topographic variation.