Comparative Assessment of Predictor Variable Combinations within Data Driven Approaches for NWP based Precipitation Forecast Enhancement

Precipitation forecasts generated by Numerical Weather Prediction (NWP) models often exhibit systematic biases arising from limitations in model resolution, representation of sub-grid-scale processes, and uncertainties in initial conditions. This study systematically assesses different predictor combinations (PC) obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) model to improve short-range precipitation forecasts using data-driven approaches over the peninsular Indian region. Different data-driven formulations, comprising of four machine learning (ML) models and two deep learning (DL) models, were implemented and systematically compared. Further, the different PCs and data driven formulations are evaluated and compared against the traditional Bayesian Model Averaging (BMA) approach, widely adopted for precipitation forecast enhancement. The improvement in precipitation forecast skill was assessed using standard deterministic and probabilistic verification metrics. The results indicate that incorporating exogenous predictor variables leads to a slight improvement in precipitation forecast skill, while DL models exhibit performance comparable to that of traditional ML models. Overall, the exogenous variable PC achieved higher forecast skill than other PCs and the traditional BMA, yielding an approximate 20% improvement in RMSE compared to 14% for the traditional BMA. Feature importance analysis revealed that total precipitation, wind speed, and 2-m temperature consistently ranked among the top five most influential variables across the different data driven formulations, underscoring the interpretability of the models.