# Amplified warming in tropical and subtropical cities under 2 °C climate change

## TL;DR

Tropical and subtropical cities are warming faster than surrounding areas under 2°C global warming, requiring better climate projections for urban regions.

## Contribution

A statistical learning model is applied to ESM outputs to project urban warming in medium-sized cities unresolved by coarse climate models.

## Key findings

- 81% of cities in the tropics and subtropics are projected to warm faster than surrounding areas under 2°C global warming.
- 16% of cities, mainly in India and China, face an additional 50-112% warming above ESM projections.
- Low-resolution climate models likely underestimate urban warming in most cities.

## Abstract

Urban heat stress under climate change is an increasing concern, as most cities are already warmer than their rural surroundings, heightening their vulnerability to rising temperatures and exposing a large share of the global population. While Global Climate Models are essential for projecting future temperature changes, their relatively coarse scale limits their ability to capture the trends of smaller cities. To bridge this gap, projected changes in land surface temperature in medium-sized cities are created and compared to surrounding regions, identifying areas where the urban warming rate is faster than rural surroundings. Our analysis shows low-resolution projections likely underestimate future urban warming in most cities, highlighting the need for deeper study.

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 and land surface types. 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. However, even high-resolution global Earth system models (ESMs) with “urban tiles” can only properly resolve the largest urban areas or megacities. Here, we address these limitations by applying a process-based statistical learning model to ESM outputs to provide projections of changes in land surface temperature (LST) for 104 medium-sized cities of population 300 K to 1 M 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. Our findings underscore the importance of investigating the specific effects of climate change on urban heat exposure.