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. We describe a methodology for ensemble member's weightingof operational seasonal forecasting systems (SFS) based on an enhancedprediction of a climate driver strongly affecting meteorological parametersover a certain region. We have applied it to the North Atlantic Oscillation(NAO) influence on the Iberian Peninsula winter precipitation. The first step in the proposed approach is to find the best estimation ofwinter NAO. Skill and error characteristics of forecasted winter NAO indexby different Copernicus SFS are analysed in this study. Based on theseresults, a bias correction scheme is proposed and implemented for the ECMWFSystem 5 ensemble mean of NAO index, and then a modified NAO index pdf basedon Gaussian errors is formulated. Finally, we apply the statisticalestimation theory to achieve the Best linear unbiased estimate of winter NAOindex and its uncertainty. For this purpose, two a priori estimates areused: the bias corrected NAO index Gaussian pdf from ECMWF System 5, and askilful winter NAO index prediction based on teleconnection with snow coveradvance with normal distributed errors. The second step of the proposed methodology is to employ the enhanced NAOindex pdf estimates for ensemble member's weighting of a SFS based on asingle dynamical model. The new NAO pdfs obtained in this work have beenused to improve the skill of the ECMWF System 5 to predict both NAO indexand precipitation over the Iberian Peninsula. We show the improvement of NAOprediction, and of winter precipitation forecasts over our region ofinterest, when members are weighted with the bias corrected NAO indexGaussian pdf based on ECMWF System 5 compared with the usual approach basedon equiprobability of ensemble members. Forecast skill is further enhancedif the Best NAO index pdf based on an optimal combination of the two apriori NAO index estimates is used for ensemble member's weighting.

Regionally improved seasonal forecast of precipitation through Best estimation of winter NAO