Due to its unique geographical environment，Qinghai Province is highly susceptible to frequent hail events. Considering the complex topography of high-altitude regions，particularly the Qinghai Plateau，this study constructs a hail forecasting dataset by integrating hail observations from 52 meteorological stations in Qinghai from 2009 to 2023，corresponding hail disaster records，and the ERA5 atmospheric reanalysis dataset. Based on this dataset，three ensemble decision tree models-Random Forest，XGBoost，and LightGBM-are employed to develop a hail forecasting model，with separate analyses conducted on hail samples with diameters of ≥2 mm and ≥5 mm. Experimental results demonstrate that the LightGBM model consistently outperforms both Random Forest and XGBoost，with particularly superior performance in forecasting large hail events（diameter ≥5 mm）. Specifically，for small hail samples（diameter ≥2 mm），the LightGBM model achieves a hit rate of 0. 923，a false alarm rate of 0. 041，a Critical Success Index（CSI）of 0. 858，an accuracy of 0. 946，and a recall rate of 0. 924，while for large hail samples（diameter ≥5 mm），it attains a hit rate of 0. 938，a false alarm rate of 0. 038，a CSI of 0. 908，an accuracy of 0. 960，and a recall rate of 0. 964. Further analysis of the hail forecasting model in the complex terrain of the plateau reveals that the most influential meteorological factors for hail fore‐ casting in Qinghai Province include thermodynamic conditions（vertically integrated temperature p54. 162，vertically integrated thermal energy p60. 162，and 2-meter dew point temperature d2m），characteristic height layer conditions（100 hPa temperature t100，400 hPa temperature t400，and 20 hPa geopotential height z20），and dynamic conditions（500 hPa zonal wind component u500，200 hPa meridional wind component v200，and 200 hPa zonal wind component u200）. Kernel density estimation analysis indicates that most feature variables exhibit limited separability，suggesting that no single factor alone can determine the occurrence of hail events. A case study demonstrates that the LightGBM-based hail forecasting model exhibits strong spatial forecasting capabilities. Analysis of the 24-hour evolution of key meteorological variables preceding a large-scale hail event at the Chaka station identifies several crucial atmospheric indicators：（1）significant fluctuations in vertically integrated temperature（p54. 162），indicating intense convective activity；（2）persistently high 2-meter dew point temperature（d2m），reflecting abundant near-surface moisture；（3）strong 500 hPa zonal wind speed（u500），suggesting enhanced mid-level atmospheric dynamics；and（4）low 100 hPa temperature（t100），capturing upper-at‐mosphere characteristics. The coordinated evolution of these atmospheric variables not only reveals key stages in the development of severe convective weather systems but also provides a scientific foundation for improving hail potential forecasting methods in Qinghai Province.