| ABSTRACT | Although most operational seasonal forecasting systems are based on dynamical models, empirical forecasting systems, built on statistical relationships between present and future at seasonal time horizons conditions of the climate system, provide a feasible and realistic alternative and a source of supplementary information. Here, a new empirical model based on partial least squares regression is presented. Originally designed as a flexible tool, the system is able to automatically select predictors from an initial pool and explore spatial fields looking for additional predictors. the model can be run with many configurations including different predictands, resolutions, leads and aggregation times. The model benefits from specific predictors for the Mediterranean region unveiled in the frame of the MEDSCOPE project. We present here 2 sets of results: the first one from a configuration producing probabilistic forecasts of seasonal (3 month averages) temperature and precipitation over the Mediterranean area, their verification and comparison against a selection of state-of-the-art seasonal forecast systems based on dynamical models in a hindcast period (1994-2015). The model is able to produce spatially coherent anomaly patterns, and reach levels of skill comparable to those based on dynamical models. To explore the potential of the model for producing skilful forecasts over reduced areas, a second set of results are calculated using higher resolution predictands over Iberia, again comparing its skill with that of a set of state of the art models. Examples of the model usage for evaluating the impact on skill of certain predictor helping in the search and understanding of new sources of predictability are also shown. |