Abstract
This work discusses the ability of a bias-adjustment method using empirical quantile mapping to improve the skills of seasonal forecasts over Europe for three key climate variables, i.e., temperature, precipitation and wind speed. In particular, the suitability of the approach to be integrated in climate services and to provide tailored predictions for local applications was evaluated. The workflow was defined in order to allow a flexible implementation and applicability while providing accurate results. The scheme adjusted monthly quantities from the seasonal forecasting system SEAS5 of the European Centre for Medium-Range Forecasts (ECMWF) by using ERA5 reanalysis as reference. Raw and adjusted forecasts were verified through several metrics analyzing different aspects of forecast skills. The applied method reduced model biases for all variables and seasons even though more limited improvements were obtained for precipitation. In order to further assess the benefits and limitations of the procedure, the results were compared with those obtained by the ADAMONT method, which calibrates daily quantities by empirical quantile mapping conditioned by weather regimes. The comparable performances demonstrated the overall suitability of the proposed method to provide end users with calibrated predictions of monthly and seasonal quantities