闪电是强对流天气中常见的灾害性放电现象，对社会经济和人民生命财产安全构成了严重威胁，高时空分辨率、及时准确的闪电临近预报预警对防灾减灾具有重要意义。系统梳理了依托观测数据开展闪电预报的研究成果。首先从物理机理角度，归纳闪电活动与各类环境气象要素之间的关联特征。在动力环境层面，整理现有研究关于上升气流、对流有效位能、垂直风切变影响电荷分离过程和闪电发生频次的相关结论；在微物理特征层面，汇总已有成果中冰晶与霰粒碰撞起电相关机理研究，以及水成物含量、降水强度表征闪电强弱的相关结论，同时梳理闪电活动在对流系统边缘、热带气旋内部的空间分布规律。其次，分类梳理依托单一观测资料构建闪电临近预报模型的技术方案。针对雷达观测资料，整理传统质心追踪、光流算法与时序网络、生成对抗网络、条件流匹配等深度学习算法在雷达回波外推、闪电反演中的各类应用研究；针对卫星观测资料，总结依托云顶演化特征识别对流初生、预估闪电发生概率的现有研究进展；同时归纳地基闪电定位网、地面大气电场仪在局地超短临预警中的应用成效。在此基础上，进一步整理多源异构观测资料融合数值预报模式、三维语义分割模型、新一代气象大模型的跨模态融合技术研究，结合Pangu-Weather、GraphCast等全球AI气象大模型与闪电高频观测相融合的探索发现，多技术融合方案可弥补单一观测短板，有效延长预报时效、提升精细化概率预报水平。最后，归纳样本失衡、模型区域适配能力弱、物理可解释性不足等难题，并对“将对流有效位能、冰相粒子等物理约束嵌入损失函数或输入通道的‘物理约束+数据驱动’”一体化智能概率预报技术的发展方向进行展望。

Lightning is a high-impact meteorological hazard associated with severe convective weather, posing substantial threats to socioeconomic activities and public safety. Timely and accurate lightning forecasting and warning with high spatiotemporal resolution are therefore of great importance for disaster prevention and mitigation. This paper systematically reviews existing research on observation-based lightning forecasting methods. First, from a physical-mechanisms perspective, it summarizes the characteristic relationships between lightning activity and various meteorological factors. Regarding the dynamical environment, previous findings are reviewed on how updrafts, Convective Available Potential Energy (CAPE), and vertical wind shear affect charge separation processes and modulate lightning frequency. Regarding microphysical characteristics, existing studies on collision-induced charging mechanisms between ice crystals and graupel particles are synthesized, along with conclusions on the roles of hydrometeor content and precipitation intensity in characterizing lightning intensity. The spatial distribution patterns of lightning activity along the margins of convective systems and within tropical cyclones are also discussed. Second, this paper classifies and reviews technical approaches for constructing lightning nowcasting models based on single-source observational data. For radar observations, it summarizes applications of traditional centroid tracking, optical flow algorithms, and deep learning methods—including temporal networks, Generative Adversarial Networks (GANs), and conditional flow matching—in radar echo extrapolation and lightning retrieval. For satellite observations, it reviews progress in identifying convective initiation and estimating lightning occurrence probability based on cloud-top evolution features. In addition, it summarizes the effectiveness of ground-based lightning location networks and atmospheric electric field mills in local ultra-short-term warning applications. On this basis, the paper further reviews studies on cross-modal fusion techniques that integrate multi-source heterogeneous observations with Numerical Weather Prediction (NWP) models, three-dimensional semantic segmentation models, and next-generation meteorological foundation models. Existing findings indicate that multi-source fusion approaches can compensate for the limitations of single-source observations, effectively extend forecast lead time, and improve the capability of refined probabilistic forecasting. Finally, this paper summarizes current challenges in lightning forecasting, including sample imbalance, weak regional adaptability of models, and insufficient physical interpretability. It also provides an outlook on future development directions for integrated intelligent probabilistic forecasting technologies that combine physical constraints with data-driven approaches.