Radar based rainfall nowcasting plays a critical role in hydrological operations such as stormwater management and flood early warning. Compared with Numerical Weather Prediction (NWP), it offers higher short-term accuracy and lower computational costs. However, operational uptake remains constrained by two key challenges: (i) uncertainties in nowcasting algorithms and (ii) discrepancies between radar rainfall estimates and ground based measurements. Focusing on the latter, this study explores the potential of the Censored Shifted Gamma Distribution (CSGD) error model to adjust high-resolution radar nowcasts using gauge observations, thus improving their hydrological applicability. The proposed framework involves calibrating both climatological and conditional CSGD models at gauge locations and interpolating parameters across the study area. Deterministic and ensemble nowcasts generated by the Short-Term Ensemble Prediction System (STEPS) are subsequently adjusted using linear and non-linear CSGD models. In this process, predicted rainfall intensities are transformed into cumulative distribution functions (CDFs), enabling probabilistic nowcasting. The median of the CSGD-derived distributions is then applied as the adjusted rainfall intensity, improving alignment with ground observations. Results suggest that combining STEPS ensemble nowcasting with the non-linear CSGD model generally yields the best performance, with error reductions approaching 6 % at the 6 h lead time (hourly scale) and at the 3 h lead time (5 min scale) and uncertainty reductions approaching 20 % across selected events. These findings demonstrate the potential of extending the CSGD method – originally developed for daily satellite precipitation estimation – to hourly and sub-hourly timescales. This advancement enhances the reliability of radar based predictions and their value for hydrological decision-making.