Study region: The study region is the J & uacute;car River Basin in eastern Spain, a regulated Mediterranean semi-arid catchment with complex topography, marked hydroclimatic variability, and intensive water management interventions. Study focus: This study quantifies the downstream impact of AI-corrected meteorological forcings on ensemble seasonal streamflow forecast performance in the J & uacute;car River Basin. Fuzzy rule-based systems were used to postprocess four global seasonal forecast systems (ECMWF-SEAS5, Meteo-France System8, DWD-GCFS2.1, CMCC-SPSv3.5) before hydrological propagation through the distributed TETIS model over 1995-2014. A dual evaluation framework (Proxy-Truth and Observed-Truth) was applied to separate meteorological and hydrological uncertainty contributions. New hydrological insights for the region under study: AI-based meteorological postprocessing improved ensemble streamflow reliability by redistributing most configurations from overconfident regimes toward an operationally optimal uncertainty zone (coverage >= 70%, R-Factor 0.75-1.5). Under Proxy-Truth and Observed-Truth, 80.6 and 77.9% of configurations, respectively, achieved enhanced coverage, with gains intensifying with lead time. Systems with severely deficient raw meteorological inputs showed the largest hydrological benefits, with CMCC-SPSv3.5 achieving 83.3% of configurations in the optimal bandwidth range and an average coverage increase of 19.8% pts. Postprocessing effectiveness on streamflow remained system-specific, as Meteo-France System8 exhibited a nominal reduction in 95PPU coverage despite a clearer redistribution of ensemble bandwidth, demonstrating that meteorological correction does not guarantee uniform hydrological gains across forecasting systems.