Global gridded dataset of heating and cooling degree days under climate change scenarios

Accurate projections of heating and cooling demands are crucial for advancing towards the sustainable development goals. Here we present a global dataset of heating degree days (HDDs) and cooling degree days (CDDs) for 3 levels of global mean temperature rise above pre-industrial conditions—1.0 °C (2006–2016), 1.5 °C and 2.0 °C—regardless of the pathways leading to these warming scenarios. The dataset comprises 30 gridded maps (0.883° × 0.556° resolution) characterizing climate variability through 5 statistical metrics per variable and scenario over a representative 10-year period. The dataset reveals a widespread decline in HDDs and a pronounced, nonlinear increase in CDDs, with the most significant shifts in climate intensity and adaptation needs emerging early in the warming trajectory. Furthermore, using the ‘middle-of-the-road’ Shared Socioeconomic Pathway 2-4.5 as a reference, the dataset indicates that the population experiencing extreme heat conditions (exceeding 3,000 CDDs) is projected to nearly double if the 2.0 °C threshold is reached, increasing from 23% (1.54 billion people) in 2010 to 41% (3.79 billion) by 2050, with the largest projected populations affected in India, Nigeria, Indonesia, Bangladesh, Pakistan and the Philippines. This HDD–CDD dataset provides a robust foundation for integrating climate information into sustainability planning and development policy. Accurate projections of heating and cooling needs are vital for planning energy use and achieving the sustainable development goals. A global dataset now maps global heating and cooling degree days, finding future hotspots, rising cooling demand and early shifts in energy needs with major global impacts.