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Seasonal hydro-meteorological forecasts are increasingly crucial for water resource management and risk mitigation in Mediterranean coastal regions, where climate variability and anthropogenic pressures create complex challenges. However, the application of global seasonal prediction systems at local scales remains limited by systematic biases and coarse spatial resolution, which hinder their operational use in decision-making processes. This contribution presents a two-level BCSD (Bias Correction Spatial Disaggregation) seasonal forecasting system applied to a complex orographic Mediterranean area.The system is based on the application of EQM (Empirical Quantile Mapping), as described by Lorenz et al. (2021), and focuses on precipitation and 2m air temperature variables from 1981 to 2024. The original product is the SEAS5 ensemble forecast released by ECMWF, with a horizontal resolution of approximately 36 km. The first level covers all of southern Italy and uses ERA5-Land as the reference dataset, yielding a horizontal resolution of approximately 9 km, whereas the second level, which can be seen as a refinement, focuses on the Calabria region and uses an observed, high-quality dataset, achieving a horizontal resolution of 5 km. Finally, a water balance model, calibrated over the Crati catchment, the main Calabrian river (southern Italy), has been intensively tested, focusing on the streamflow prediction. The performance has been evaluated using bias, spatial correlation, and the CRPSS (Continuous Ranked Probability Skill Score) metrics.The results reveal systematic biases in raw SEAS5 predictions, with precipitation consistently underestimated by up to 20 mm/month, particularly during transitional months (May, June, and September), whereas 2m air temperature exhibits a persistent warm bias of approximately +1°C. The first-level BCSD correction substantially reduces these errors across southern Italy, yielding positive CRPSS values for precipitation, especially during the summer season (JJA), and marked improvements in temperature predictions (CRPSS > 0.40). The spatial correlation increases, with precipitation average increasing by 19% and temperature by 16%. However, compared with observation, residual biases persist at the catchment scale, with winter precipitation (NDJ) remaining underestimated by more than 60 mm/month over the Crati, and autumn temperatures (SON) slightly overestimated. The implementation of the second-level BCSD effectively addresses these local-scale discrepancies, enhancing the spatial correlation and CRPSS skill scores and ensuring that the hydrological model receives minimally biased forcings, predicting realistic streamflow.This two-stage correction framework demonstrates the system's capability to preserve probabilistic forecast skill while enabling reliable impact assessments through the hydrological modeling chain, thereby bridging the gap between global seasonal predictions and local water resource management applications. As a further step, the first-level BCSD has been operationally implemented and is freely available at https://cesmma.unical.it/cwfv2/seasonal.html, as requested by several local stakeholders. In the future, the second-level BCSD and the hydrological impacts evaluation will be operationally implemented.Reference: Lorenz, C., et al. Bias-corrected and spatially disaggregated seasonal forecasts: a long-term reference forecast product for the water sector in semi-arid regions, Earth Syst. Sci. Data, 13, 2701–2722, https://doi.org/10.5194/essd-13-2701-2021, 2021.