Lightning activity is usually associated with precipitations events and represents a possible indicator of climate change, even contributing to its increase with the production of NOx gases. The study of lightning activity on long temporal periods is crucial for fields related to atmospheric phenomena from intense rain-related hazard processes to long-term climate changes. This study focuses on 19 years of lightning-activity data, recorded from Italian Lightning Detection Network SIRF, part of the European network EUCLID (European Cooperation for Lightning Detection). Preliminary analysis was dedicated to the spatial and temporal assessment of lightning through detection in the Central Mediterranean area, focusing on yearly and monthly data. Temporal and spatial features have been analyzed, measuring clustering through the application of global Moran’s I statistics and spatial local autocorrelation; a Mann–Kendall trend test was performed on monthly series aggregating the original data on a 5 × 5 km cell. A local statistically significant trend emerged from the analysis, suggesting possible linkage between surface warming and lightning activity.

The relation between the increase in the frequency and the effects of extreme events with climate change has been widely demonstrated and the related consequences are a global concern. In this framework, the strong correlation between significant lightning occurrence and intense precipitation events has been also documented. Consequently, the possibility of having a short-term forecasting tool of the lightning activity may help in identifying and monitoring the evolution of severe weather events on very short time ranges. The present paper proposes an application of Random Forest (RF), a popular Machine Learning (ML) algorithm, to perform a nowcasting of Cloud-to-Ground (CG) lightning occurrence over the Italian territory and the surrounding seas during the months of August, September, and October from 2017 to 2019. Results obtained with three different spatial resolutions have been compared, suggesting that, to enhance the skills of the model in identifying the presence or absence of strokes, all the data selected as input should be commonly gridded on the finest available spatial resolution. Moreover, the features’ importance analysis performed confirms that meteorological features describing the state of the atmosphere, especially at higher altitudes, have a stronger impact on the final result than topology data, such as Latitude or Digital Elevation Model (DEM).

The climate change of the last decades is causing an increase in the frequency of extreme event occurrences. Numerous studies confirm the link between extreme meteorological events and lightning activity. The possibility of having short-term predictions of the intensity of lightning phenomena would allow the near-real-time monitoring of the evolution of these events. Such knowledge would help in defining operational strategies to mitigate effects on the population and the infrastructure. In this context, this article proposes a multidisciplinary approach aiming at developing a regression algorithm to nowcast the density of cloud-to-ground lightning strokes 1 h in advance. The algorithm is developed on the basis of a joint combination of remote sensing data, numerical weather prediction model outcomes, and lightning observations. The possible dependence between the meteorological data and the density of lightning is estimated using Gaussian process regression models. Separate models are created for positive and negative strokes and for strokes occurrences over the land or sea. The experimental results indicate that the model quite accurately estimates low numbers of strokes, whereas larger numbers of strokes are estimated with higher errors. Nevertheless, their presence is correctly detected. This suggests the potential of the method as a processing tool to support the management of weather-related hazards.

This study investigates the evolution of lightning activity in Italy by comparing two biennia: 2010–2011 and 2020–2021. Lightning data were obtained from a lightning location system (the SIRF network) and processed to focus exclusively on cloud-to-ground (CG) strokes. The analysis covers both land and surrounding sea areas, with data filtered and validated according to the study objectives. A detailed statistical comparison of CG activity between the two periods is presented, along with an assessment of spatial distributions across the Italian territory. Furthermore, correlations between CG stroke occurrence and both sea surface temperature and land surface temperature are examined. The findings highlight temporal and spatial variations in lightning patterns over the decade and provide insights into their possible relationship with environmental conditions.