Has the increase in temperatures led to an increase in the risk of heat-related deaths in Europe due to climate change? Are humans to blame for global climate change and global warming? Has the way we use hydrocarbons and fossil fuels brought these natural disasters upon us humans? Haven't climatologists and hydrologists already warned that the pace of climate change has now increased? Do you think there is a solution in the current situation?

A warming climate is expected to significantly increase heat-related mortality across Europe, with notable differences between urban and nonurban areas, as well as among regions. In this modelling study, we projected daily mortality from January 1, 2015, to December 31, 2050, under four climate change scenarios (Shared Socioeconomic Pathways [SSP] SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Projections utilized localized, age- and region-specific exposure-response functions and high-resolution temperature projections statistically downscaled from three global-scale general circulation models to address regional variability and uncertainties. Our results indicate a three-fold increase in the mean heat-related mortality rate by 2040–2050 compared to the 2015–2030 baseline, with annual excess deaths ranging from 3 to 35 per 100,000 people under SSP1-2.6 to 9 to 46 per 100,000 under SSP5-8.5, depending on the region. Southern Europe is projected to experience the largest increases, particularly under high-emission scenarios (SSP3-7.0 and SSP5-8.5), while Western Europe shows a more stable trend. Sustainability scenarios (SSP1-2.6) yield moderate increases, underscoring the potential benefits of low-carbon development pathways. Uncertainty in heat-related mortality projections was largely driven by variations across climate models, emphasising the need for robust climate adaptation and mitigation strategies tailored to regional and demographic vulnerabilities. Targeted interventions, particularly in Southern Europe, are critical to reducing the disproportionate impact of warming temperatures on population health.Anthropogenic emissions of greenhouse gases have induced a rise in global temperatures, culminating in severe heatwaves and elevated summertime temperatures (GarcíaLeón et al. 2021). As per the findings of the Copernicus Climate Change Service (C3S) (Buontempo et al. 2022), 2024 had a global average temperature of 15.10 °C, 0.12 °C higher than the previous highest annual value in 2023. The adverse health effects of heatwaves are extensively documented in the literature, with empirical studies highlighting their profound impact on population mortality rates worldwide (Zhang et al. 2018; Son et al. 2019; De Troeyer et al. 2020; Ballester et al. 2023; Fonseca-Rodríguez et al. 2023). Vicedo-Cabrera et al. (2021) estimate that anthropogenic climate change was responsible for 37.0% (range: 20.5–76.3%) of heat-related deaths across 43 study countries, with significant regional variation. Projections indicate an exacerbation of health burdens, particularly under the most extreme global climate change scenarios (Yang et al. 2021). Given the multifaceted nature of climate-related health risks, numerous factors including climate, environmental, and population changes, as well as socioeconomic conditions, behaviour, and lifestyle can influence an individual’s susceptibility to heat (Masselot et al. 2023). This complexity makes it difficult to generate accurate mortality estimates for both present and future scenarios (Rohat et al. 2019). One of the main challenges in studying climate-related risks is that Earth System Models (ESMs) (Kawamiya et al. 2020) have a low-resolution grid, which fails to capture local phenomena that play a crucial role in the occurrence of natural hazards and their impacts (Li et al. 2017). To address this, downscaling methods either dynamic or statistical are commonly employed (Casanueva et al. 2016). Studying and projecting the future impacts of climate change across different regions of Europe is essential for identifying and prioritizing adaptation measures (van Daalen et al. 2024). However, existing studies often focus on specific countries or cities, with only a few adopting a broader perspective (Jenkins et al. 2022; De Schrijver et al. 2023; Masselot et al. 2023). Furthermore, most research primarily examines historical or current impacts, leaving a gap in climate projections (Ballester et al. 2023), particularly in the assessment of multiple climate models and scenarios over extended periods. This study aims to provide a reliable and consistent estimate of the mortality burden associated with suboptimal temperature conditions through 2050. Unlike previous assessments, our approach combines four Shared Socioeconomic Pathways (SSPs) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) with projections from three different climate models, allowing for a thorough examination of future mortality risks under a variety of climatic and socioeconomic scenarios. The key innovation is the use of an advanced statistical downscaling technique to derive high-resolution daily mean temperatures at 2 m, which ensures greater accuracy in exposure assessment. Furthermore, we use a large dataset of exposure-response relationships that account for age-related risks, which improves the granularity of our mortality projections. By combining these methodological advancements, we present a refined framework for predicting heat-related mortality, which provides critical insights for policymakers on climate change and public health. 2 Methods Within the framework of the European Project DISTENDER, five European regions (Fig. 1), were selected to study heat-related excess mortality. Focusing on Austria, the EURAF region (encompassing Montado-Dehesa region in the Iberian Peninsula), the North-east of The Netherlands, The Metropolitan City of Turin (CMTo) (Italy), and the Northern Portugal region, this study spans different characteristics in terms of activity sectors, scale, climate impacts, environmental, socioeconomic, and cultural factors, and climate policy goals. This diversity of case studies will ensure the maximum replicability of this work. The spatial resolution of each domain is as follows: Austria (9 ×9 km), EURAF region (9 ×9 km), North-east of The Netherlands (1 ×1 km), the CMTo (1 ×1 km), and Northern Portugal region (1 ×1 km). 2.1 Climate Projections and Temperature Despite improvements in computing power, global climate models still have coarse spatial resolutions (~ 100 km), limiting their usefulness for predicting local climate changes. There are two main downscaling methods: dynamical, which uses high-resolution regional models driven by global model data, and statistical, which requires less computing power and can provide local climate assessments In the scope of DISTENDER project, a cutting-edge statistical method was developed to produce climate scenarios (1981–2050) of a large set of variables, including temperature of three CMIP6 Earth System models: CanESM5, EC-EARTH3, and MPI-ESM1-2-HR were selected as representative models from a pool of ten CMIP6 candidates, based on their projected temperature trajectories by 2050. In particular, the method included three steps: (1) Parametric quantile mapping; (2) hourly transfer function; and (3) Geostatistical downscaling. Firstly, a parametric quantile mapping (Monjo et al. 2014) was used to locally transfer reference probability distribution to the Historical and the four main SSP experiments (SSP1-2.6, SSP2-4.5, SSP3- 6.0, SSP5-8.5) of the CMIP6 Earth System Models at a daily scale. For this purpose, ERA5-Land reanalysis data (0.073°×0.073°) was used as a reference. In the second step, for each modelled (targeted) day, the most analogous past day was selected from the reanalysis by comparing their spatial thermal patterns to each targeted day of every climate projection (from the first step) and then linear transfer functions were applied from the maximum/minimum values of each projected day to the hourly curve of its analogous day, so producing an hourly climate projection of that targeted day at the reference 0.073°×0.073° grid. The last step consists of a geostatistical technique with multi-linear AICbased stepwise regression, fitting high-resolution predictors (land use, geographical and topographical parameters), with a final bilinear interpolation for the residual errors (Monjo et al. 2024). This particular statistical downscaling approach was preferred over alternatives due to its lower computational demands compared to dynamical downscaling. Given the large number of climate projections required in this project, statistical downscaling offers a more feasible and efficient solution. Population and Mortality Data: We obtained population data information from the Gridded Population of the World dataset 9 , which includes population density for every 5-year age range for 2010 and total population counts for 2010, 2015 and 2020 at 30 s horizontal resolution. These data were processed to get a gridded population for each domain/region resolution, for 2020, aggregated by age group (age groups from 20 to 44, 45–64, 65–74, 75–84, and 85+). We sourced mortality data for each country/domain from EUROSTAT’s ‘Deaths by Week and Sex’ product.10 Since the data was provided on a weekly basis, we calculated the daily mortality rate by dividing the weekly value by 7.Mortality Impact Assessment A central component in estimating heat-related mortality is the exposure-response function (ERF), which quantifies the statistical relationship between ambient temperature and mortality risk. This function typically exhibits a non-linear pattern, where mortality increases significantly above or below an optimal temperature threshold. ERFs allow for the translation of projected temperature changes into expected changes in health outcomes, making them essential tools for climate-health impact assessments. In the context of climate change projections, the estimation of future heat-related mortality relies on several interacting elements: the shape and slope of the ERF, the projected trajectory of temperature increases under various climate scenarios, baseline mortality rates for specific causes, and demographic information such as the size and age structure of the exposed population. These factors inherently carry uncertainties, which propagate through the projections and affect the confidence of future estimates (Chen et al. 2017). In this study, we utilized ERFs tailored to five age groups (20–44, 45–64, 65–74, 75–84, and 85 +years), which were derived from the recent work of Masselot et al. (2023). These age-specific functions were developed using advanced statistical modelling techniques that account for both historical temperature and mortality data, enhancing the reliability of the estimates. Incorporating age-stratified ERFs is particularly important, as vulnerability to heat increases markedly with age, and failure to account for this heterogeneity can result in significant under- or overestimation of the health burden. Figure 2 displays the proven exposure-response connections for various age groups, which indicate the relative risks (RR) of mortality associated with average temperature. The curves exhibit the well-known inverted J pattern, with mortality staying comparatively low until it approaches the considerably high Minimum Mortality Temperature (MMT). From this point on, as the temperature rises, the chance of dying increases. The susceptibility of populations to high temperatures is reflected in this tendency. The curves for groups 65 and older are relatively similar in shape, but they get steeper with age, indicating that older age groups are more vulnerable. The lowest temperature in a given population at which the risk of death is reduced is referred to as MMT. By comparing the MMT specifically for the age group 85 +across the different case studies it is possible to observe that in Austria, the MMT for individuals aged 85 and above is 21.3. In EURAF, the MMT is 25.88. Northern Portugal shows an MMT of 19.19 for individuals aged 85 and above. The Netherlands has an MMT of 18.02 for this age group. Turin displays an MMT of 19.26 for individuals aged 85 begin to emerge, with higher-emission scenarios (SSP3-7.0 and SSP5-8.5) showing a faster rate of warming, and consequently more heat-related deaths. These variation across scenarios become more pronounced in the final period (2040–2050), when SSP3-7.0 and SSP5-8.5 show higher temperature increases, when compared to the more moderate scenarios (SSP1-2.6 and SSP2-4.5). These patterns are also influenced by regional differences in the intensity and pace of warming, which vary across Europe. For example, in the Northern Portugal domain, total excess deaths range from 82 to 228, depending on the SSP scenario and time period. The greatest numbers are seen in the northeast of the Netherlands (HUAS), reaching 3357 per year for the SSP585 scenario for the time period 2040–2050. These findings underscore the significance of adopting ambitious climate mitigation strategies, as lower-emission pathways are associated with fewer heat-related deaths. The influence of climate policy becomes increasingly evident, with a marked reduction in mortality rates under scenarios with stronger emissions reductions. Standardized risk summaries account for demographic differences, providing a more equitable geographical comparison than unadjusted excess death rates. The excess death rates vary significantly among scenarios and regions. and above. These comparisons highlight notable differences in the MMT values for the oldest age group across different regions, indicating variations in heat-related mortality risks and potentially differing levels of vulnerability to extreme temperatures. The ERFs provide insights into the impact of maximum temperatures on mortality risks. When temperature values exceed the maximum threshold, we conservatively assume that the corresponding RRs remain constant, leading to a more cautious estimation. In Austria, the maximum temperature threshold is 30.36 °C. The corresponding RRs for the age groups 20–44, 45–64, 65–74, 75–84, and ≥85 years are 1.47, 1.61, 1.68, 1.75, and 1.82, respectively. For Northern Portugal, the maximum temperature threshold is 30.35 °C, resulting in RRs of 1.45, 1.58, 1.67, 1.73, and 1.80 for the same age groups. In the Netherlands, the maximum temperature threshold stands at 28.97 °C, corresponding to RRs of 1.78, 1.92, 1.99, 2.06, and 2.13. EURAF registers a maximum temperature threshold of 32.3 °C, with corresponding RRs of 1.45, 1.59, 1.67, 1.74, and 1.81. CMTo records a maximum temperature threshold of 31.15 °C, resulting in RRs of 1.62, 1.76, 1.84, 1.90, and 1.97 for the respective age groups. We computed the attributable daily deaths (DD) due to heat exposure for each domain grid cell as follows:Where: ● DD represents the estimated daily heat-related deaths at the domain level. ● bi is the county-level baseline daily mortality rate. ● Tmeani is the daily average temperature projection from each model and SSP for the county. ● ERF(Tmeani ) is the daily mortality for a givenmeani . ● Pi is the population cell. To obtain the annual excess deaths, the sum of attributable daily deaths across all cells and days of the year is calculated.Results The produced climate simulations were approved by the Kolmogorov-Smirnov test performed (Massey 1951) for the historical period. It is a non-parametric test, meaning it does not assume a specific underlying distribution for the data. Projections estimated a reduction of total precipitation amount in summer (CMTo) and autumn (HUAS and Austria) between 10 and 20%, and of more than 40% during the warmest months in Guimaraes and EURAF. An increase of precipitation up to 20% is expected in CMTo in Spring, while Winter will be 10–30% wettest in Guimaraes and HUAS. Maximum temperature will increase up to +3.5ºC in Guimaraes and Austria, and up to +5ºC in CMTo, HUAS and EURAF during summer under the SSP5-8.5 scenario. A comprehensive overview of the findings is presented in Table 1, covering both the total number of excess deaths due to heat and the corresponding excess death rates (per 100,000 person-years). The total number of excess deaths shows the overall burden in each region, while the death rates make it easier to compare regions with different population sizes. This assessment spanned across five domains, encompassing a population exceeding 50 million inhabitants across multiple countries. The regions analysed include Northern Portugal (1,303,000 inhabitants), CMTo (2,535,000 inhabitants), EURAF (15,255,000 inhabitants), Austria (24,560,000 inhabitants), and HUAS (7,366,000 individuals aged over 20, using population data from 2020). The number of excess deaths varies by scenario and time period, following the projected temperature changes for each SSP scenario. For the period 2015–2050, temperature projections show a general warming trend across all SSP scenarios, but the rate of increase differs between the scenarios and across the time. In the early period (2015– 2030), the temperature increase is relatively similar across the four scenarios, leading to similar heat-related mortal ity results. However, between 2030 and 2040, differences begin to emerge, with higher-emission scenarios (SSP3-7.0 and SSP5-8.5) showing a faster rate of warming, and consequently more heat-related deaths. These variation across scenarios become more pronounced in the final period (2040–2050), when SSP3-7.0 and SSP5-8.5 show higher temperature increases, when compared to the more moderate scenarios (SSP1-2.6 and SSP2-4.5). These patterns are also influenced by regional differences in the intensity and pace of warming, which vary across Europe. For example, in the Northern Portugal domain, total excess deaths range from 82 to 228, depending on the SSP scenario and time period. The greatest numbers are seen in the northeast of the Netherlands (HUAS), reaching 3357 per year for the SSP585 scenario for the time period 2040–2050. These findings underscore the significance of adopting ambitious climate mitigation strategies, as lower-emission pathways are associated with fewer heat-related deaths. The influence of climate policy becomes increasingly evident, with a marked reduction in mortality rates under scenarios with stronger emissions reductions. Standardized risk summaries account for demographic differences, providing a more equitable geographical comparison than unadjusted excess death rates. The excess death rates vary significantly among scenarios and regions.Some locations record very low rates, ranging from 3 to 9 per 100,000 person-years, while others have much higher rates, ranging from 24 to 46 per 100,000 person-years. The Metropolitan City of Turin (CMTo) and the northeast of the Netherlands (HUAS) have greater rates than Austria and Northern Portugal, which have lower values. Generally, there is an increasing trend in both total excess deaths and excess death rates over the assessed periods, especially towards the later years (2040–2050), across all SSP scenarios and domains. This indicates a potentially worsening impact of heat-related mortality in the future. Overall, the findings demonstrate the importance of taking into account several scenarios and domains when estimating the potential impact of heat-related mortality, as well as the need for appropriate mitigation and adaptation measures to address this public health concern. Figure 3 depicts box plots showing the predicted excess of overall mortality for each research domain, taking into consideration four climatic scenarios and three time periods: 2015-2030, 2030-2040, and 2040-2050. All case study’s findings show the same basic pattern: a rise in mortality over time, with a particularly apparent increase between 2030 and 2040 and 2040 and 2050. When comparing climate scenarios, there is also a noticeable rise in mortality, with the SSP3-7.0 and SSP5-8.5 scenarios producing more heat-related mortality than the SSP1-2.6 and SSP2-4.5 scenarios due to higher levels of emissions and global warming, respectively. Appendix A includes charts displaying the variations year by year across the whole period (Fig. A.1). The analysis of results was also conducted individually for each climate model, with comprehensive details provided in the Appendix (Fig. A.2). Significant variations were observed in the results obtained from the temperature data generated by the three climate models: CanESM5, ECEARTH3, and MPI-ESM1-2-HR. Each model has its own distinct strengths and weaknesses. Notably, the CanESM5 model appears to more accurately replicate the anticipated temperature increases resulting from the climate scenarios. This is evident in the clear rise in heat-related mortality observed from SSP1 to SSP5. Figure 4 displays the maps of Excess Mortality for each of the study domains, focusing on a specific year (2049), utilizing the CanESM5 model, and under the climate scenario (SSP5-8.5). Northern Portugal has three different urban centres: Porto, Braga, and Guimarães. In Porto and Braga, there are around 8 deaths per grid cell with a resolution of 1× 1 km². CMTo’s spatial resolution (1 ×1 km²) enables precise observations of the city centre, where estimates reach 10 deaths per cell in 2049, especially under the worst-case climate scenario. In the EURAF domain, which includes sections s. Furthermore, substantial variations are noted in the results when considering temperature projections from the three climate models used (CanESM5, EC-EARTH3, and MPI-ESM1-2-HR). In general, the MPI-ESM1-2-HR model provides lower estimates of heat-related deaths, while the EC-EARTH3 model offers higher estimates. Each of these models has its strengths and weaknesses, but the CanESM5 model appears to better capture the expected temperature increases across all case studies, showing a clear rise in heat-related mortality from SSP1 to SSP5. The divergences among the three climate models for certain scenarios highlight the importance of using an ensemble of models for a more dynamic and robust analysis. Variations in vulnerability can be attributed to several things, such as the local temperature, the effects of urban heat islands, the accessibility of healthcare, and land use, especially the presence of green areas (Macintyre et al. 2018; Yadav et al. 2023). Previous research has found associations between heat susceptibility and characteristics such as PM2.5 exposure, population density, and economic inequality (Masselot et al. 2023; Ataee et al. 2025). The study regions reveal diverse but interconnected vulnerabilities to heat, influenced by climate, urban structure, and demographics. Turin’s dense urban layout and limited green areas intensify urban heat island effects, especially affecting its aging population. Northern Portugal shares of Madrid (Spain) and Lisbon (Portugal) and has a resolution of around 9× 9 km², mortality rates can reach up to 40 per cell. Notably, death rates in Lisbon are lower than in Madrid, most likely due to its closeness to the Atlantic Ocean. In the Austria domain, which includes key cities such as Vienna (Austria), Munich (Germany), and Bratislava (Slovakia), Vienna has the largest number of deaths, potentially topping 70 deaths per grid cell at a 9 ×9 km² resolution. In the North-east Netherlands, calculations with a resolution of 1× 1 km² revealed Amsterdam as having more than 50 deaths, according to the CanESM5 model and the SSP5-8.5 climatic scenario. Heat impacts varied greatly among domains, with relatively low standardized mortality rates in Austria and northern Portugal and much higher rates in the Metropolitan City of Turin (CMTo) and North-east of The Netherlands (HUAS). Furthermore, a slight coastline effect was seen, with locations near the shore, such as the EURAF region, experiencing less heat-related effects.Discussion and Conclusions This study provides an assessment of the mortality consequences linked to temperature changes in five different European domains (Austria, the EURAF region (encompassing the Montado-Dehesa region in the Iberian Peninsula), the North-east of The Netherlands, the metropolitan area of Turin (Italy), and the Northern Portugal region). The study employs state-of-the-art methodologies to precisely assess the health effects of temperature across a significant 35-year period (2015–2050) under four different potential Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). This paper presents extensive evaluations for each location using temperature data derived from a statistical downscaling of global climate models and local-specific risk functions. When the results from the five domains are analysed, there is a definite increase in heat-related excess mortality when comparing different climate scenarios. The more severe scenarios, such as SSP3-7.0 and SSP5-8.5, which involve greater global warming, result in much more heatrelated mortality than the SSP1-2.6 and SSP2-4.5 scenarios, with an average increase of +18% to +41% over the entire time. These findings are in line with previous pan-European studies, such as Kendrovski et al. (2017) who estimated an increase in annual heat-related deaths to between 46,690 (RCP 4.5) and 117,333 (RCP 8.5) by the end of the century, and with the more recent work by Ignjačević et al. (2024) which projected up to 5.5 million cumulative heat-related deaths in urban areas of Europe over the 21 st century if mitigation efforts fail. The consistent trend observed across these studies and our regional case studies highlights the significant and growing public health burden associated with future warming, particularly under high-emissions scenarios. Despite the differences among the case studies, the overall trend is consistent: there is a steady increase in heat-related deaths as one moves from SSP1 to SSP5. For the SSP5-8.5 scenario, the increase in heat-related deaths peaks between 2040 and 2050, with the following increases compared to the 2015–2030 period: For Northern Portugal, the central percentage increase is 178%, with a confidence interval ranging from 75 to 322%, indicating notable variability in the data. Similarly, CMTo shows a central percentage increase of 95%, with a confidence interval between 51% and 150%. The EURAF case study exhibits a central increase of 153.45%, with a confidence interval spanning from 77 to 266%, suggesting a moderate to high increase. In Austria, the central percentage increase is 157%, with the confidence interval ranging from 57 to 329%, reflecting a broad range of possible outcomes. Finally, HUAS demonstrates the highest central percentage increase at 193%, with a confidence interval from 84 to 365%, highlighting significant uncertainty. This trend aligns with the projected rise in daily average temperatures, a key factor driving the anticipated health implications associated with these changes.Finally, this study, which takes into account variations in vulnerability, sheds insight on the issue of heat-related excess mortality in five European areas. The findings are critical for guiding policymakers as they develop national, regional, and local public health and climate change strategies. Urban heat resilience should be prioritized within the European Green Deal and the EU Strategy on Adaptation to Climate Change, ensuring that health risks are systematically addressed in climate resilience planning. Moreover, a coordinated European approach is needed to embed health impact assessments into adaptation policies, aligning climate mitigation and adaptation with long-term public health objectives. The results underscore the urgency of translating scientific evidence into targeted policy actions to protect vulnerable populations. This study provides a fresh assessment of how temperature affects health in Europe under projected future climate scenarios