Abstract
Cities are often warmer than rural surroundings due to a phenomenon known as the urban heat island, which can be influenced by various factors, such as regional climate. Under climate change, cities face not only the challenge of increasing temperatures in their surrounding hinterland, but also the challenge of potential changes in their heat islands. Making projections of future climate at the city scale is difficult given limitations of Earth System Model (ESMs), which has limited studies to a small number of urban areas – mostly megacities. Here, we address these limitations by applying a novel process-based machine learning model to ESM outputs, to provide projections of changes in land surface temperature (LST) for 104 medium-sized cities (population 300K to 1M) in the subtropics and tropics. Under a 2°C global warming scenario, annual mean LST in 81% of these cities is projected to increase faster than the surrounding area. In 16% of these cities, mostly in India and China, mean LST is projected to increase by an additional 50–112% above ESM projections of the surrounding area. These findings suggest that the potential impacts of climate change are underestimated at present for millions of people in cities.