This study focuses on the impact of climate variability and climatic change on energy consumption. Two objectives are being pursued simultaneously. First, we are using statistical analysis of GCM simulations and observed surface and upper-atmosphere climate parameters to relate the course-grid predictions of GCMs to regional scales. The second goal is to use analysis of historical energy consumption and climate data to establish statistical models relating consumption to climate parameters such as temperature (and degree days), humidity, wind speeds, and sky cover. An integral part of this research effort involves combining the output of these two objectives to produce reliable estimates of how climatic change will impact energy consumption. Climate data have been obtained for eight of the most energy-intensive states in the U.S. For each state we have more than 10 years of daily climate data from 5 to 8 stations. Corresponding to these climate data we have gathered the relevant electricity and natural gas consumption data. We have conducted a preliminary analysis of GCM results from both the CCM1 andthe CCM2 models. Results with the CCM2 show a strong correlation between upper-atmosphere model predictors and surface climate predictands. The implication is that through suitable statistical analysis we will be able to generate regional climate change predictions. The methods and preliminary results from these two related projects will be presented.