The Western Water and Energy project is exploring the use of seasonal climate forecasts in managing water and energy resources in the U.S. west. In this talk we will describe seasonal temperature and energy demand forecasts arising from this project. The energy industry is an important part of the economy that is strongly affected by climate variability. Temperature has a strong influence on energy demand; for instance, in summer, hot temperatures drive air conditioning use, which can constitute 40% of the total electrical demand on warm afternoons. In winter, cold temperatures drive increased natural gas consumption for space heating. It follows that seasonal temperature forecasts can be transformed to seasonal energy demand forecasts by using a relationship between temperature and demand. Such forecasts can be useful to decision makers in the energy sector, for example by allowing a more efficient allocation of resources between short-term spot market energy purchases and long-term energy contracts. Several examples of climate predictions developed specifically for this problem this will be presented. In the winter, the North Pacific Oscillation (NPO) influences temperatures over the western U.S., accounting for about 5% of the variability in regional heating degree days (a energy utility measure of the amount of energy demand expected for heating), which represents about $220M in natural gas consumption. Part of this is predictable based on persistence of the NPO. In summer, the influence of the NPO can also be felt, especially along the U.S. west coast. Again, statistical prediction schemes show that some of this summer variability (Jun-Jul-Aug) can be predicted based on sea surface temperature (SST) conditions in May. In the interior of the U.S., spring soil moisture conditions can predict part of the variability in summer temperatures. The end result is a prediction system that can forecast part of the variability in seasonal energy demand based on antecedent SST and soil moisture conditions, providing information that may allow decision makers run the energy system more efficiently.