Global modeling studies of future climate change predict large scale climatic responses to increased atmospheric carbon dioxide (CO2). While there have been several regional climate modeling studies that produced results at spatial and temporal scales relevant for climate change impact analysis, few have employed statistical significance testing of results. In a sensitivity study that focused on mean climate states, we use a regional climate model to generate ensembles of climate scenarios under atmospheric conditions of 280 and 560 ppm CO2, for a domain centered over California. We find statistically significant responses by mean annual and monthly temperature, precipitation, and snow to CO2 doubling. Relative to the 280 ppm results, 560 ppm results show temperature increasing everywhere in the region annually (up to 3.8¿C), and in every month, with the greatest monthly surface warming at high elevations. Snow accumulation decreased everywhere, and precipitation increased in northern regions by up to 23%, on a mean annual basis.