Understanding the spatial and temporal variability of heatwaves is crucial for climate change adaptation. This study examines heatwaves in Argentina from 1950 to 2022, analyzing temporal and spatial changes using four metrics: number of events (E), duration (D), mean intensity (MnI), and maximum intensity (MxI). It investigates seasonal variations (Warm and Cold Seasons—CS, WS) and the influence of different phases of the El Niño-Southern Oscillation (ENSO). Data from ERA5-LAND Reanalysis for 2 m daytime (Tx) and nighttime (Tn) temperatures are utilized. Our findings reveal regions with significantly higher heatwave intensities (Tx) in the North, east of Cuyo, west of Centro, and Southern Patagonia. Conversely, significant heatwave intensities (Tn) were observed, particularly in the north of the Litoral and Southern Patagonia. The Andes region (center and north) exhibited significant intensities for Tn. Both D and E exhibited similar significant trends for both Tn and Tx, except for the central zone. During the WS, the North-West and South Patagonia exhibit significant increasing trends for across most metrics. In contrast, during the CS, a higher number of significant increases in the studied metrics were observed in relation to Tx. El Niño amplifies heatwave intensities nationwide, except in Patagonia, where this occurs during the cold phase. In this phase, E and D of events increase in most Argentinian regions, resulting in a decoupling of intensity and duration, which increases in opposite periods. This study contributes to existing research by providing a detailed understanding of heatwave behavior with high spatial resolution.

Climate change remains a critical global challenge, yet denialism endures across various sectors despite growing scientific consensus. Climate misinformation poses a major obstacle, with radio podcasts—an influential medium—contributing to its spread. This study, conducted in collaboration with C3/IU-RESCAT/URV, VERIFICAT, Kinzen, and Chequeado, represents the first analysis of climate mis- and disinformation narratives in Spanish-speaking podcasts. Over eight months, podcasts were monitored, focusing on content from June to November 2022, coinciding with COP27. The dataset comprised 629 podcast excerpts, totalling over 241 h. The study aimed to identify, categorise, and assess the prevalence of climate misinformation narratives. The podcast selection phase proved to be the most critical stage of the workflow. The most frequent climate misinformation narratives were: ‘Climate science is unreliable (conspiracy)’, ‘Climate change does not exist’, and ‘Solutions do not work’. These were predominantly present in programmes categorised under Politics, Economy & Opinion, and Mystery & Other Realities. Notably, the ‘Greenwashing’ narrative appeared exclusively in Politics, Economy & Opinion content. During COP27, the daily rate of climate mis- and disinformation claims (7.5 per day) was significantly higher than during the regular period (2.9 per day). Podcasts showed heightened scepticism towards the effectiveness of climate solutions, while conspiracy-driven doubts about climate science and outright denial decreased. These findings are consistent with previous analyses of social media, highlighting the pervasive nature of climate misinformation across platforms. The study reinforces the importance of confronting such narratives to counter climate denial effectively.

This study analyzes the Surface Urban Heat Island (SUHI) effect and the spatial distribution of Land Surface Temperature (LST) during heatwaves in three climatically and geographically distinct Argentine cities: Posadas, Buenos Aires, and Neuquén. Leveraging high-resolution satellite imagery (MODIS, Landsat 8, Sentinel-2) and the Local Climate Zone (LCZ) classification, the research quantifies SUHI intensity and identifies fine-scale thermal patterns across urban, peri-urban, and rural areas. Results show that all three cities experience significant SUHI effects, particularly at night, with urban areas consistently exhibiting higher LSTs than their surroundings. However, daytime patterns vary considerably. In Neuquén’s semi-arid plateau, a negative SUHI was observed, with rural areas reaching higher temperatures than urban centers. Densely built environments—especially compact zones—demonstrate elevated heat retention, exacerbating heatwave impacts. Conversely, areas with substantial vegetation, such as parks and river corridors, consistently show lower surface temperatures, underscoring their role in urban cooling. By integrating multi-source remote sensing data with the LCZ framework, this study provides a robust and transferable methodology for analyzing intra-urban thermal variability. The findings offer valuable insights for climate adaptation and urban planning, particularly in rapidly expanding cities facing increasing exposure to extreme heat. Graphical Abstract This study examines the effects of extreme heat in urban environments by analyzing the Surface Urban Heat Island (SUHI) phenomenon and the spatial distribution of Land Surface Temperature (LST) across three climatically diverse Argentine cities: Posadas, Buenos Aires (CABA), and Neuquén. The research integrates high-resolution satellite imagery (MODIS, Landsat, Sentinel) and Local Climate Zone (LCZ) classification to capture fine-scale thermal dynamics during heatwaves. The two main goals were: (1) to assess SUHI intensity in different urban contexts, and (2) to explore how surface temperature varies within cities based on land cover and built environment characteristics. The results show that densely built zones, particularly compact LCZs, experience higher LSTs both during the day and at night. Vegetated and peri-urban areas consistently exhibit lower surface temperatures, confirming the cooling role of green infrastructure. In Posadas and CABA, the SUHI effect is strongest at night, while Neuquén presents a unique case of daytime negative SUHI, especially in semi-arid rural plateaus with low vegetation and soil moisture. The findings highlight how local geography, urban morphology, and vegetation interact to shape heat exposure, emphasizing the need for spatially targeted heat mitigation strategies. These insights are particularly valuable for guiding urban planning and climate adaptation efforts in rapidly expanding cities facing growing risks from extreme heat events.

Heatwaves are intensifying across Mediterranean cities, where the Urban Heat Island (UHI) effect amplifies thermal stress. This study updates the spatial characterization of the Surface Urban Heat Island (SUHI) in Tarragona using multi-sensor remote sensing data within a Local Climate Zone (LCZ) framework. Land surface temperature, albedo, and the Normalized Difference Vegetation Index (NDVI) were analyzed during heatwaves from 2015–2025 to assess spatial patterns and drivers of urban heating. Results reveal a daytime urban cool island associated with low albedo and scarce vegetation, and a nocturnal SUHI caused by heat retention in dense built-up areas. High-resolution mapping identifies industrial and commercial zones as hotspots, while vegetated and water-covered areas act as cooling sites. These findings clarify the spatial dynamics and key biophysical controls of SUHI and provide an actionable basis for prioritizing locally tailored adaptation strategies in Mediterranean coastal cities.

In this research, based on a case study of Ukraine, we (1) examined the sensitivity of heatwave (HW) climatology to input gridded data and (2) statistically compared HW metrics (such as duration, intensity, etc.) calculated from the gridded data against similar results derived from high-quality station time series. For the first task, we used a mini statistical ensemble of gridded datasets of the daily maximum air temperature (TX). The ensemble included the following: ClimUAd and E-OBS (Ukrainian and European observation-based gridded data, respectively), reanalyzes ERA5, ERA5-Land, NOAA-CIRES 20CR V2c and V3, and NCEP-NCAR R1. For the second task, the same gridded data were used along with 178 quality-controlled and homogenized TX station time series from Ukraine. HWs and their metrics were defined according to the approach summarized by Perkins and Alexander (2013). All calculations were performed for the period 1950–2014. Our results showed that, depending on the gridded dataset, the calculated values of HW metrics might differ significantly. Even after averaging over the study period and the territory of Ukraine, the ranges between the max and min values of HW metrics remain large. For instance, the spread in HW number per year may be up to six events. However, the differences in the trend slopes of HW metrics are less pronounced. In addition, the comparison of HW calculations derived using gridded and station data showed that E-OBS, ERA5, and ERA5-Land provide similar verification statistics. The evaluation statistics for 20CRV3 are worse compared to E-OBS, ERA5, and ERA5-Land, but significantly better than for 20CRV2c and NCEP-NCAR R1. Our findings can aid in selecting gridded datasets for calculating reliable HW climatology and, consequently, contribute to developing climate adaptation strategies for extreme temperature events in Ukraine, its neighboring countries, and potentially across Europe.

In the previous decade, different WMO expert teams drafted the Competencies for the Provision of Climate Services (WMO, 2019). In awareness of the importance of Climate Services, the Universitat Rovira i Virgili's Bachelor's Degree on in Geography, Territorial Analysis features two courses specifically mapped to this competency framework. Strategically placed in the 2nd and 3rd year, these courses offer 12 ECTS, equipping students with core knowledge and skills for delivering climate services. The curriculum includes a participatory methodology, encouraging co-creation, user engagement, and responsiveness to user needs, as highlighted in research by Font et al. (2021). This approach, contrasting traditional educational models, fosters a dynamic, interactive learning environment. Students undertake a workflow in line with the WMO's framework for climate service provision. Project 3, the first course, focuses on the first two core competencies: C1 Create and Manage Climate Data Sets (covering quality control and data homogenization) and C2 Derive Products from Climate Data. This first course establishes an understanding of climate data management and product development and introduces the students to programming applications. The second course, "Project 5: Co-creation of Climate Services", further develops the coding skills and emphasizes communication skills (C5, Communicate Climatological Information with Users). It reviews database creation, and the development of products tailored to sectoral areas of local significance, like tourism or cultural events or high-priority areas identified by the Global Framework for Climate Services (GFCS), such as Health. The program's emphasis on co-creation and sector-specific applications highlights its commitment to practicality and real-world relevance in climate service fields. Competency development in these courses is assessed against various learning outcomes aligned with the WMO's guidelines for climate service provision. Student progress is evaluated through diverse criteria, adhering to a "learning by doing" approach. This methodology provides a hands-on experience and facilitates continuous skill improvement guided by instructor feedback. This experiential learning model is crucial for integrating theoretical knowledge with practical application, preparing students for professional challenges in climate-related fields.