Catalonia, a southern Europe coastal region in the western Mediterranean basin, is regularly affected by severe local storms which every year produce an average of 5 tornadoes and 12 waterspouts, based on data recorded from 2000 to 2016. Despite these tornadoes mostly being weak – not exceeding EF1 on the enhanced Fujita (EF) damage intensity scale – they occasionally reach stronger intensities, posing an evident threat to population and socio‐economic activity, particularly in densely populated coastal conurbations such as Tarragona or Barcelona. This study provides analysis of upper air conditions that favour the formation of tornadic storms in the region, considering five different stations in Spain and France to build a database of 333 soundings which included 49 tornadic soundings, 104 waterspout soundings (WAT) as well as non‐tornadic thunderstorms and dry period soundings. For each one, 12 different parameters were calculated and analysed to test their potential capacity to discriminate non‐tornadic from tornadic environments and also to assess the feasibility of stratifying tornado intensity (EF0, and EF1 or stronger, on the EF scale). The results indicate that storm‐relative helicity for the layer 0–3 km, with a threshold value of 150 m² s⁻², wind shear for the same layer, with a threshold of 15 m s⁻¹, and composite parameters such as the supercell composite parameter with a threshold of 1.2 and universal tornadic index with a threshold of 0.3 are all useful to distinguish between non‐tornadic thunderstorms and EF1 or stronger tornadic events. These results contribute to a better description of the climatology of upper air conditions that favour tornadic storms in the region and can also help operational weather forecasting and surveillance tasks, thus increasing situational awareness in severe convective weather events.

A dataset of 907 tornado and waterspout events recorded from 1980 to 2018 was built to study convective environments in the Iberian Peninsula and Balearic Islands (western Mediterranean). The events were grouped into different categories, distinguishing waterspouts and tornadoes that were stratified by intensity according to the Fujita (F) scale and the Enhanced Fujita (EF) scale. The analysis separated the north‐east (NE) and south‐west (SW) subareas in the region of study, which present different seasonal cycles. For each event, atmospheric profiles from the ERA5 reanalysis data were used to determine convective available potential energy (CAPE), storm‐relative helicity (SRH), vertical wind‐shear (WS), the Universal Tornadic Index (UTI), and the product of wind‐shear and the square root of two times CAPE (WMAXSHEAR). Results showed that the NE events are mostly associated with higher CAPE and lower helicity and wind‐shear than the SW events. Thus, a significant number of SW tornadoes are associated with high‐shear, low‐CAPE environments. Moreover, the low‐shear, high‐LCL tornado environment, which is common inland during warm‐season, is more usual in the NE subarea. Composite parameters such as the UTI and WMAXSHEAR₀₆ are good discriminators between significant and weak tornado events, although WMAXSHEAR₀₆ presents some limitations for the SW events due to low CAPE and weak differences in the WS (0–6 km) between the (E)F1 and (E)F2+ events. This weakness was resolved by using the 0–3 km WS instead of the 0–6 km WS when calculating the WMAXSHEAR. A new threshold for WMAXSHEAR₀₃ is proposed (500 m²⋅s⁻²) to distinguish between significant and non‐significant tornado environments. Finally, the Szilagyi Waterspout Nomogram, originally developed for the Great Lakes of North America, was successfully tested in the forecasting of waterspout formation for the first time in the western Mediterranean area, although the technique should be adapted to correctly detect cool‐season mid‐latitude waterspouts.

Post-event damage assessments are of paramount importance to document the effects of high-impact weather-related events such as floods or strong wind events. Moreover, evaluating the damage and characterizing its extent and intensity can be essential for further analysis such as completing a diagnostic meteorological case study. This paper presents a methodology to perform field surveys of damage caused by strong winds of convective origin (i.e. tornado, downburst and straight-line winds). It is based on previous studies and also on 136 field studies performed by the authors in Spain between 2004 and 2018. The methodology includes the collection of pictures and records of damage to human-made structures and on vegetation during the in situ visit to the affected area, as well as of available automatic weather station data, witness reports and images of the phenomenon, such as funnel cloud pictures, taken by casual observers. To synthesize the gathered data, three final deliverables are proposed: (i) a standardized text report of the analysed event, (ii) a table consisting of detailed geolocated information about each damage point and other relevant data and (iii) a map or a KML (Keyhole Markup Language) file containing the previous information ready for graphical display and further analysis. This methodology has been applied by the authors in the past, sometimes only a few hours after the event occurrence and, on many occasions, when the type of convective phenomenon was uncertain. In those uncertain cases, the information resulting from this methodology contributed effectively to discern the phenomenon type thanks to the damage pattern analysis, particularly if no witness reports were available. The application of methodologies such as the one presented here is necessary in order to build homogeneous and robust databases of severe weather cases and high-impact weather events.

In the climate change context, wildfires are an increasing hazard in the Mediterranean Basin, especially those triggered by lightning. Although lightning activity can be predicted with a reasonable level of confidence, the challenge remains in forecasting the thunderstorm’s probability of ignition. The present work aims to characterise the most suitable predictors to forecast lightning-ignited wildfires. Several ERA5 parameters were calculated and compared for two different samples, thunderstorm episodes that caused a wildfire (n = 961) and ordinary thunderstorms (n = 1023) that occurred in Catalonia (NE Iberian Peninsula) in the 2006–2020 period. Lightning wildfires are mostly associated with dry thunderstorms, characterised by: weak-to-moderate Mixed-Layer Convective Available Potential Energy (MLCAPE, 150–1100 J kg⁻¹), significant Dew Point Depression at 850 hPa (DPD₈₅₀, 3.3–10.1 °C), high Most-Unstable Lifted Condensation Level (MULCL, 580–1450 m) and steep 500–700 hPa Lapse Rate (LR, −7.0–−6.3 °C). Under these conditions, with relatively dry air at lower levels, thunderstorms tend to be high-based, the rain evaporating before reaching the ground and lightning occurring without significant rainfall. Specifically forecasting the probability of LIW occurrence would be of great assistance to the forest protection tactical decision-making process, preparing for “dry” thunderstorm days where multiple ignitions can be expected.

Lightning Mapping Array (LMA) networks are used to detect very high frequency (VHF, 60–66 MHz) electromagnetic sources associated with lightning channels inside clouds, providing three-dimensional lightning data. Over the past decades, LMA has been used in scientific campaigns with the aim of improving our understanding of electrification processes and revealing 3D lightning signatures related to severe weather. The use of real-time LMA data in weather surveillance and nowcasting provides valuable additional information to weather forecasters, playing an essential role in early warning alerts alongside other remote sensing systems such as traditional lightning detection networks, radar, and satellite. Since 2023, an operational LMA network has been running in real time in Catalonia (northeastern Iberian Peninsula) as a collaboration between the Meteorological Service of Catalonia (SMC) and the Technical University of Catalonia (UPC). The XCALMA (extended Catalonia LMA) network consists of 30 LMA stations, spanning from the coast to the Pyrenees to cover the entire 32,000 km² country, making it the largest LMA network in Europe. Several severe weather signatures can be detected using LMA data. One of them is the presence of isolated VHF detections in the overshooting top (OT), observed throughout the entire OT lifecycle. These detections, also known as sparkles, have been shown in some studies to precede surface severe weather reports—even occurring earlier than the commonly used lightning jump signature. In this study, we focus on the analysis of a selection of severe storms reported in Catalonia between 2023 and 2025 that exhibited this signature, including large-to-giant hail and tornadic events. We combine LMA data with C-band radar and satellite observations to evaluate the presence of lightning in the OT as a potential predictor of severe weather.