A Spatiotemporal XGBoost Framework for Segment-Level Lightning Nowcasting Along a High-Speed Railway Corridor

Lightning poses localized risks to signaling, communication, and traction-power systems along high-speed railways, yet most nowcasting products are generated on regular grids rather than operational line segments. This study developed a grid-to-segment machine-learning framework for next-hour lightning warning along the Jinan West–Tai’an corridor. Ground-based lightning observations, hourly ERA5 fields, temporal variables, and engineered historical lightning features and spatial-neighborhood features were organized on a 0.25° grid. Data from 2014 to 2018 were used for training, 2019 for validation, and 2020 for independent testing. Grid probabilities were converted into warnings for six railway segments using 10 km buffers and maximum-probability aggregation. In 2020, the full extreme gradient-boosting (XGBoost) model, a tree-based ensemble-learning algorithm, achieved grid-level probability of detection (POD), false-alarm ratio (FAR), and critical success index (CSI) values of 0.55, 0.50, and 0.36; segment-level verification yielded 0.60, 0.39, and 0.43. To examine transfer to forecast-driven application, the trained model and threshold were fixed, and ERA5 meteorological inputs were replaced by short-lead ECMWF HRES forecasts for July 2025. POD decreased from 0.85 to 0.80 and CSI from 0.60 to 0.57, while FAR remained nearly unchanged. Under a predefined non-zero rule, HRES litota1 achieved 0.62, 0.71, and 0.25. ML-HRES therefore showed higher CSI and lower FAR than the direct litota1 baseline.