Traditional methods for studying thunderstorm climatology and associated hazards often rely on analyzing long-term trends of key environmental variables. These variables are derived from low-resolution reanalysis datasets like ERA5 and are aggregated over extended time periods (Taszarek et al., 2021). The methods also derive probability of occurrence of convective events, providing useful insights into large-scale climatological trends (Battaglioli et al., 2023). However, they struggle to capture the fine-scale characteristics of individual events. Their representations tend to suffer from probabilistic smoothing effects—such as overestimating hazard occurrence in regions without activity and underestimating it in areas of high activity—leading to unrealistic distributions of hazards in space, time, and intensity.Machine learning approaches, including random forests, gradient boosting and more recently deep learning models, have improved short-term lightning nowcasting (McGovern et al., 2023). However, they rely on high-resolution inputs such as radar and satellite data which constrains their usage because of the spatio-temporal limitation of such data. To address this limitation, we develop a deep learning model tailored for reanalysis data which enables us to apply the model on a global scale and over a much larger period of time to study the evolution of thunderstorm activity.Given a state of the atmosphere, we generate an ensemble forecast of lightnings at an hourly time resolution and 0.25° spatial resolution. Compared to previous models (Battaglioli et al., 2023, Saha et al., 2025), we use 3D inputs in our model to directly output a map of lightning with statistically coherent spatial structures. Additionally, we make ensemble predictions to capture a wide range of possible realistic scenarios for a given set of thermodynamic and dynamic variables.Technically, we develop a guided diffusion model that learns to generate lightning maps at an hourly timestep over western Europe. Given 3 maps of environmental conditions (representing instability, humidity and wind-shear) derived from the ERA5 reanalysis and an ensemble of 2D noise data, we generate an ensemble of possible lightning maps.Because of the large temporal availability of ERA5 data, our model will enable us to detect changes in past thunderstorm activity.
Burq et al. (Fri,) studied this question.