Flood forecasting in Mediterranean catchments is challenging due to strong rainfall variability and rapidly evolving antecedent wetness conditions. These challenges are further amplified in data-scarce basins, where limited hydrometric observations constrain the application of conventional real-time forecasting approaches. This study develops a probabilistic Rainfall-Runoff Database (RRDB) for the Crati River basin (2440 km²) in Calabria, southern Italy. The framework combines stochastic rainfall and air-temperature generation with a continuous semi-distributed hydrological model to generate, offline, a large ensemble of precomputed hydrographs spanning different event durations, precipitation amounts, and antecedent soil moisture conditions. The RRDB is evaluated across the three gauging sections in the study basin: Cosenza-Ponte Europa, Castiglione Cosentino, and Sibari using observations of precipitation, satellite soil moisture and streamflow. The stochastic rainfall generator reproduces the main statistical properties and extremes of observed precipitation, while the hydrological model performs well across the evaluated gauging stations, with Nash-Sutcliffe efficiency (NSE) = 0.85 and Kling-Gupta efficiency (KGE) = 0.93 at the Sibari calibration section. Independent comparison of the modelled soil moisture with Advanced SCATterometer (ASCAT) satellite observations yields correlation coefficients of 0.50–0.72 across the three sub-catchments, supporting the use of satellite observations to characterize basin antecedent wetness conditions. Event-based validation shows that most observed hydrographs, peak discharges, and runoff volumes fall within or close to the corresponding RRDB probabilistic envelopes, although some extreme peaks are underestimated. The RRDB is evaluated in operational mode during the February 2026 flood event through its integration into HYPES (Hydro Prediction Engine System), an operational decision-support and flood forecasting platform. In this setup, forecast rainfall and satellite-derived surface soil moisture are used to identify representative precomputed scenarios within the RRDB and retrieve probabilistic discharge forecasts in real time. At Sibari, the observed peak discharge of 663 m³ s^-1 falls within the interquartile range of the retrieved probabilistic forecast (497–840 m³ s^-1), with an expected value of 674 m³ s^-1.