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. In the last years coupled atmospheric ocean climatemodels have remarkably improved medium range seasonal forecasts, especiallyon middle latitude areas such as Europe and the Mediterranean basin. In thisstudy a new framework for medium range seasonal forecasts is proposed. It isbased on circulation types extracted from long range global ensemble modelsand it aims at two goals: (i) an easier use of the information contained inthe complex system of atmospheric circulations, through their reduction to alimited number of circulation types and (ii) the computation of high spatialresolution probabilistic forecasts for temperature and precipitation. Theproposed framework could be also useful to lead predictions ofweather-derived parameters, such as the risk of heavy rainfall, drought orheat waves, with important impacts on agriculture, water management andsevere weather risk assessment. Operatively, starting from the ensemblepredictions of mean sea level pressure and geopotential height at 500  hPa ofthe NCEP – CFSv2 long range forecasts, the third-quantiles probabilisticmaps of 2  m temperature and precipitation are computed through a Bayesianapproach by using E-OBS 0.25 ∘ gridded datasets. Two differentclassification schemes with nine classes were used: (i) Principal ComponentTransversal (PCT9), computed on mean sea level pressure and (ii) SimulatedAnnealing Clustering (SAN9), computed on geopotential height at 500  hPa .Both were chosen for their best fit concerning the ground-levelprecipitation and temperature stratification for the Italian peninsula.Following this approach an operative chain based on a very flexible andexportable method was implemented, applicable wherever spatially andtemporally consistent datasets of weather observations are available. In this paper the model operative chain, some output examples and a firstattempt of qualitative verification are shown. In particular three casestudies (June 2003, February 2012 and July 2014) were examined, assumingthat the ensemble seasonal model correctly predicts the circulation typeoccurrences. At least on this base, the framework here proposed has shownpromising performance.

A new framework for probabilistic seasonal forecasts based on circulation type classifications and driven by an ensemble global model