Recent advances in AI-based weather forecasting models, such as FourCastNet, Pangu-Weather, and GraphCast, have demonstrated the remarkable ability of deep learning to emulate complex atmospheric dynamics. Building on this momentum, we propose Mj\"olnir, a novel deep learning-based framework for global lightning flash density parameterization. Trained on ERA5 atmospheric predictors and World Wide Lightning Location Network (WWLLN) observations at a daily temporal resolution and 1 degree spatial resolution, Mj\"olnir captures the nonlinear mapping between large-scale environmental conditions and lightning activity. The model architecture is based on the InceptionNeXt backbone with SENet, and a multi-task learning strategy to simultaneously predict lightning occurrence and magnitude. Extensive evaluations yield that Mollnir accurately reproduces the global distribution, seasonal variability, and regional characteristics of lightning activity, achieving a global Pearson correlation coefficient of 0.96 for annual mean fields. These results suggest that Mj\"olnir serves not only as an effective data-driven global lightning parameterization but also as a promising AI-based scheme for next-generation Earth system models (AI-ESMs).

本研究解决了深度学习在气象预测模型中对全球闪电活动表征不足的问题。提出的Mjölnir框架通过训练ERA5气象预测因子和全球闪电定位网络的观测数据，能够有效捕捉大规模环境条件与闪电活动之间的非线性关系。研究结果表明，Mjölnir可准确重现全球闪电活动的分布、季节变异性及区域特征，为下一代地球系统模型提供了一个有前景的AI基础解决方案。

Mjölnir：全球闪电频率的深度学习参数化框架