Rising extreme heat is driving an unprecedented surge in cooling demand, with projections indicating that the energy needed for cooling by 2050 could equal the combined electricity capacity of the US, EU, and Japan in 2016. The Paris Agreement's 1.5 °C target is increasingly unattainable, raising concerns about the consequences of exceeding this limit and reaching 2.0 °C. This study investigates the additional cooling demand resulting from this increase, a crucial question given the consensus that avoiding warming to 1.5 °C is unlikely. Cooling degree days (CDDs), which measure how much a location's daily mean temperature exceeds a baseline (usually 18 °C), are used to quantify cooling demand. The study maps annual CDDs and identifies countries most affected by warming from 1.5 °C to 2.0 °C, using both absolute and relative increases in CDDs as indicators. Previous research has primarily used historical data or focused on specific regions, leaving a gap in global projections for 1.5 °C and 2.0 °C scenarios. This study addresses this gap by using 2100 simulations of global mean surface temperature through the HadAM4 model across three scenarios: historical (2006–2016), 1.5 °C, and 2 °C, utilizing 700 simulations per scenario via the climateprediction.net (CPDN) citizen-science project. The high temporal and spatial resolution of the data (6-hourly mean temperatures at 0.883° × 0.556° resolution) allows for a detailed analysis of cooling demand variations across the globe.

Prior research on CDDs has primarily utilized historical data or focused on specific geographic regions. While studies have examined CDDs in Europe and China under various scenarios (e.g., RCP4.5 and RCP8.5), these analyses often lacked a global perspective or did not consider relative changes in CDDs, which are crucial for understanding adaptation challenges in regions not traditionally accustomed to extreme heat. The current study builds upon this existing literature by providing a comprehensive global analysis of CDD changes under specific warming scenarios (1.5 °C and 2.0 °C), offering a unique perspective on relative and absolute cooling demand shifts across countries, considering population density. This global model-based analysis considers the temporal resolution to be 6 hourly, much more granular than the previous studies.

This study employed the HadAM4 Atmosphere-only General Circulation Model (AGCM) from the UK Met Office Hadley Centre to simulate 700 members for each of three global warming scenarios: historical (2006–2016), 1.5 °C, and 2.0 °C above pre-industrial levels. The simulations, part of the climateprediction.net (CPDN) project, generated 6-hourly mean temperatures at a spatial resolution of 0.883° × 0.556°. Cooling degree days (CDDs) were calculated using a baseline temperature of 18 °C. The analysis focused on absolute changes (abs-ACDD) in CDDs between the 1.5 °C and 2.0 °C scenarios to pinpoint areas with severe increases in heat exposure and relative changes (rel-ACDD) to identify regions facing substantial adaptation challenges due to their unfamiliarity with increased heat. Countries were ranked based on area-weighted mean CDD changes to avoid distortion from grid-specific values. The study considered countries with populations over 5 million for the primary analysis and over 2 million in supplementary analyses, which included urban area-weighted analyses. Data processing and analysis were performed using Python and QGIS. Bias correction utilized ERA5 hourly data.

The results revealed a significant increase in cooling demand worldwide with an increase from 1.5 °C to 2.0 °C. Sub-Saharan Africa experienced the largest absolute increase in CDDs. Ten African countries showed the most substantial absolute changes, mainly situated in a central west-east band bordering Mauritania, Niger, and Sudan—regions historically experiencing high extreme heat. In contrast, the Global North experienced the most dramatic relative increases. Switzerland and the United Kingdom showed the highest relative changes (30%), highlighting significant adaptation needs in regions traditionally unprepared for sustained high temperatures. Extended data reveals further significant relative increases in the Andes mountain range and the Himalayas, indicating the need for further research on climate change and cooling demands in these regions. Supplementary analyses focused on urban areas, ranking Ireland, the UK, and Finland as the most affected countries. The results utilized an ensemble of 700 simulations per scenario and the highest available temporal resolution to ensure robustness and detail.

The study's findings emphasize the disproportionate impact of even a small increase in global warming on cooling demand. The substantial absolute increases in CDDs in Africa underscore the need for focused research on equitable access to cooling and resilience measures in the region, given limited prior studies on this escalating threat. The significant relative increases in the Global North highlight the need for substantial adaptation efforts in countries with limited experience in managing prolonged heat. The results highlight the need for global collaboration to address this global challenge, urging policymakers to accelerate progress towards the 1.5°C target to mitigate the potentially catastrophic consequences. The findings underscore the importance of considering both absolute and relative changes in cooling demand when planning for climate adaptation.

This study reveals the significant increase in global cooling demand that will result from a rise in global mean temperature from 1.5 °C to 2.0 °C. The disparities in absolute and relative changes highlight the varying needs for adaptation across the globe. Immediate and substantial investments in localized climate adaptation strategies are crucial to mitigate the negative consequences of increased heat exposure, ensuring equitable access to sustainable cooling solutions, especially in the Global South and regions historically less equipped to manage extreme heat. Further research should incorporate additional variables (humidity, solar irradiance, etc.) for more accurate cooling demand estimations and should explore different thermal comfort expectations among communities.

While the study used a large ensemble of simulations and high-resolution data, the findings are based on a model-based prediction. Uncertainties remain regarding the precise future impacts of climate change and the complex interplay of socio-economic factors that influence cooling demand. Future studies should integrate socio-economic and behavioral factors for a more comprehensive understanding of cooling needs.