| ABSTRACT | Global seasonal forecasts of precipitation are currently produced by the major weather centers. These predictions are available several months in advance at horizontal resolutions of 200 km grid-size. They have proved useful to providing an estimate of the expected precipitation over large areas. However, their value is limited for regional applications, for example, hydrological applications such as water resources planning and flood forecast in areas characterized by complex terrain, where information at finer temporal and spatial resolutions is required. Downscaling of global precipitation forecasts to the regional scale is possible through statistical and dynamical approaches. Each of these strategies possesses advantages and limitations in physical, computational and real-time implementation aspects; these have been widely reviewed and discussed in literature. For instance, statistical downscaling is computationally cheap but it relies on reliable long-term records of observed precipitation. These may be sparsely distributed. In contrast, dynamical downscaling techniques which produce regional scale gridded precipitation forecasts using regional climate model nested down from global models, may fill the gaps in sparsely observed areas, but the technique is computationally demanding, in particular if real-time forecasts are desired. The present work combines statistical downscaling methods and dynamical information to provide realtime seasonal forecasts of precipitation and temperature at high horizontal resolution. The statistical downscaling it is based on dynamical properties of the system using analogs, which makes use of predictors from CMCC model outputs and high resolution observations/reanalysis data in several variables of interest for hydrological or agriculture application in the Mediterranean region. A comparative discussion of this methodology and preliminary results will be presented. These forecasts will serve hydrological/agriculture applications, where, for instance, the water budget strongly depends on the fine spatial distribution of seasonal precipitation. |