Improving Probabilistic Lightning Forecasts through Ensemble Postprocessing with Mesoscale Information

Accurate short-term lightning forecasting requires reliable representations of both lightning occurrence and intensity, as well as the underlying convective processes. While ensemble prediction systems (EPS) provide valuable probabilistic information, their ability to resolve mesoscale and convective-scale variability remains limited. In this study, we assess the added value of mesoscale information for probabilistic lightning forecasting over eastern China. A mesoscale ensemble is constructed from deterministic forecasts of the China Meteorological Administration (CMA) Mesoscale Model (MESO) using spatiotemporal neighborhood and time-lagging techniques and is combined with predictors from the CMA regional ensemble prediction system (REPS). Lightning occurrence and counts are modeled within a Bayesian additive model for location, scale, and shape (BAMLSS) framework, using a hurdle-based count regression to account for excess zeros and overdispersion. Influential nonlinear predictors are selected via stability selection combined with gradient boosting. Forecast performance with and without MESO-derived predictors is systematically evaluated. The results show that incorporating mesoscale information consistently improves forecast skill for both lightning occurrence and intensity across all verification metrics. These improvements are primarily associated with MESO predictors related to convective available potential energy and convective precipitation, highlighting the importance of mesoscale processes for probabilistic lightning forecasting.