Weather and climate forecasts have been steadily improving, but only limited parallel improvements in the hydrologic application of this information have been made. Long range probabilistic forecasts of precipitation and temperature have been routinely made by the NWS Climate Prediction Center (CPC) for periods beginning with the next month and for 3 month periods out for at least the next year. Procedures to apply these forecasts to make ensemble streamflow forecasts have been developed and are beginning to be used as part of the AHPS implementation plan. However, these techniques should be more closely examined to understand their limitations and perhaps improved. Weather and climate uncertainty information for all forecast lead times up to seasonal and inter-annual is beginning to be improved through ensemble forecast techniques. Procedures to process ensemble precipitation forecasts to make them suitable for input to hydrologic forecast models are being developed and tested by the NWS Office of Hydrologic Development (OHD) in collaboration with the National Centers for Environmental Prediction (NCEP) and in collaboration with the Office of Global Programs (OGP) GAPP project. This collaboration involves the Environmental Modeling Center (EMC), The Hydrometeorological Prediction Center (HPC) and the Climate Prediction Center (CPC), OAR's Climate Diagnostics Center (CDC) and the academic community. OHD is developing an ESP Pre-Processor that produces ensemble precipitation forecasts required by existing hydrologic forecast models currently used in ESP. This ESP Pre-Processor will use existing operational NCEP/HPC and NCEP/CPC deterministic and probabilistic forecast products for lead times ranging from 6 hours out to 1 year. Existing ESP pre-processor capabilities should be extensively tested and evaluated to fully and comprehensively assess strengths and limitations and to identify required improvements. New ESP Pre-Processor procedures need to be developed to use NCEP/EMC ensemble forecasts. Because quantitative weather and climate forecast models used to create ensemble forecasts tend to be biased and because space and time scales of weather and climate forecasts are generally larger than the space and time scales of hydrologic forecast models, weather and climate forecasts must be rescaled (to account for forecast biases) and downscaled (in space and time). This requires an archive of ensemble forecasts and corresponding precipitation observations and research is needed to determine how long this period should be. An important aspect of the application of the hydrologic application of weather and climate forecast information is that the skill of weather and climate forecasts increases as the forecasts are aggregated in space and time. This means that both the skill and uncertainty associated with these forecasts is space-time dependent. Because hydrologic applications exist over a wide range of space and time scales it is essential that the space time structure of weather and climate forecast information be both understood and accounted for in operational forecast systems.