Major weather centers, such as National Center for Environment Prediction (NCEP) and ECMWF, produce inter-seasonal weather predictions 6 - 9 months ahead. However, the products from these centers have ~200 km grid sizes, which are too coarse for regional applications. For hydrological applications, such as flood forecasting, watershed control, and water resource planning, detailed spatial and ... [Show full abstract] temporal distributions of precipitations are very critical. Existing precipitation downscaling approaches include statistical downscaling algorithms (SDA) and dynamical downscaling algorithms (DDA). SDAs are mostly based on regression using reanalysis and/or hindcasts and may apply for future forecast downscaling. SDAs impose three assumptions: a) the past regression relation is (static) valid for the future, b) there is no feedback of local physical forcing (terrain, coastlines and land-use/soil properties) in response to weather/climate changes and c) downscaling valid at the stations where long historical observations are available. DDAs, by which a regional climate model is embedded (nested) in a global seasonal model, overcome many of the shortcomings of a SDA. However, DDAs are computationally costly and data handling is complicated. In this paper, we present a dynamic-enforced statistical downscaling algorithm (DESDA) for effectively downscaling global-model seasonal forecasts. Four steps are involved with DESDA: 1) using the NCAR Four-Dimensional Data Assimilation (FDDA) modeling system, built upon the Weather Research and Forecasting (WRF) model, to produce 1 - 4 km gridded climatological precipitation-distribution analyses over the eastern Mediterranean region, driven by global analyses; 2) calibrating the gridded model precipitation with available precipitation measurements; 3) Applying an advanced KNN based regression downscaling approach based on the calibrated high-resolution gridded precipitation analysis, NCEP global analysis, and NCEP climate forecasting system (CFS) model 29 years of reforecasts for downscaling the CFS seasonal forecasts of precipitation anomalies; and 4) reconstructing precipitation amounts of the seasonal forecasts on the high-resolution WRF analysis grids. The algorithm and preliminary results will be presented.