为了提高复杂地形区域的 2 m 高度最高气温预报精度，基于 LightGBM（Light Gradient BoostingMachine，LGB）算法针对四川盆地及其周边的复杂地形，开展了2 m最高气温的梯度建模研究。通过对多个气象要素和地形因子的选取与分析，构建了最优模型。研究表明：（1）2024年1-6月，LightGBM模型相比EC模式，平均绝对误差减少了2. 48 ℃，预报准确率提高了36. 97%。其中，川西高原和攀西地区的提升效果最为显著，准确率分别提高了67. 2%和57. 5%。（2）与现有的客观预报产品SPCO和SCMOC相比，LightGBM模型的预报准确率分别提升了5. 1%和10. 3%。尤其在攀西地区和四川盆地，个别站点的预报效果提升最大，分别达 17. 6% 和 23. 4%。（3）LightGBM 模型按月的平均绝对误差减少了 2. 05~2. 78 ℃，准确率提升了31. 1%~41. 0%，其中4月的提升效果最为显著。（4）LightGBM模型具备良好的扩展性，未来可通过引入时间滞后效应、空间邻域特征，并结合分区建模与多模型集成，进一步提高四川省及其各区域的气温预报精度。

To enhance the prediction accuracy of the 2-meter maximum temperature in complex terrain areas， this study developed a gradient modeling approach based on the LightGBM（Light Gradient Boosting Machine， LGB）algorithm，applied to the Sichuan Basin and its surrounding regions. By selecting and and analyzing multiple meteorological and topographic factors，an optimized model was constructed. The results demonstrate that： （1）From January to June 2024，the LightGBM model reduced the mean absolute error by 2. 48 ℃ and improved the forecast accuracy by 36. 97% compared to EC model. Among them，the improvement effect of the west Sichuan Plateau and Panxi area was the most significant，the accuracy rate increased by 67. 2% and 57. 5%，respectively.（2）Compared with the existing objective forecast products SPCO and SCMOC，the LightGBM model im‐ proved prediction accuracy by 5. 1% and 10. 3%，respectively. Particularly in the Panxi area and the Sichuan Ba‐ sin，the accuracy at individual stations improved by up to 17. 6% and 23. 4%，respectively.（3）The LightGBM model reduced the mean absolute error by 2. 05~2. 78 ℃，and increased the accuracy by 31. 1%~41. 0%，with the most notable enhancement occurring in April.（4）The LightGBM model exhibits strong scalability. Future work could further improve temperature prediction across Sichuan Province and other regions by incorporating time-lag effects，spatial neighborhood characteristics，and combining zoning modeling and multi-model integration.