Projections from general circulation model (GCM) simulations must be downscaled to the high spatial resolution needed for assessing local and regional impacts of climate change, but uncertainties in the downscaling process are difficult to quantify. We employed a multiple linear regression model and the MM5 dynamical model to downscale June, July, and August monthly mean surface temperature over eastern North America under greenhouse gas-driven climate change simulation by the NASA GISS GCM. Here we examine potential sources of apparent agreement between the two classes of models and show that arbitrary parameters in a statistical model contribute significantly to the level of agreement with dynamical downscaling. We found that the two methods and all permutations of regression parameters generally exhibited comparable skill at simulating observations, although spatial patterns in temperature across the region differed. While the two methods projected similar regional mean warming over the period 2000–2087, they developed different spatial patterns of temperature across the region, which diverged further from historical differences. We found that predictor domain size was a negligible factor for current conditions, but had a much greater influence on future surface temperature change than any other factor, including the data sources. The relative importance of SD model inputs to downscaled skill and domain-wide agreement with MM5 for summertime surface temperature over North America in descending order is Predictor Domain; Training Data/Predictor Model; Predictor Variables; and Predictor Grid Resolution. Our results illustrate how statistical downscaling may be used as a proxy for dynamical models in sensitivity analysis.