A groundbreaking study led by the University of East Anglia (UEA) has unveiled alarming projections on how tropical and subtropical cities are poised to warm at rates exceeding earlier expectations as global temperatures climb towards the critical 2°C benchmark. Published in the prestigious Proceedings of the National Academy of Sciences (PNAS), this research employs an innovative combination of cutting-edge climate change models and sophisticated machine learning techniques to reveal the amplification of urban heat islands in numerous medium-sized cities around the world, predominantly in monsoon-affected regions such as India, China, and Western Africa.
Urban heat islands, a well-documented phenomenon, describe the elevated temperatures in cities compared to their rural surroundings due to factors like urban infrastructure, vegetation loss, and localized heat retention. While urban heat islands have been studied extensively, this study delivers new insights into how climate change could exacerbate these effects in medium-sized cities, which have been overlooked in most global climate assessments that tend to focus on megacities. The research specifically targets 104 cities with populations between 300,000 and one million, offering a deeper understanding of urban warming trends in these critical yet understudied urban centers.
The methodology integrates state-of-the-art global climate projections with advanced machine learning models to forecast changes in daytime land surface temperatures. This approach surpasses conventional climate models that often lack the spatial resolution to accurately capture the complex dynamics of smaller cities. By bridging this gap, the study elucidates that a staggering 81 percent of the examined cities are predicted to experience greater temperature increases compared to their adjacent rural areas. Even more striking is the finding that approximately 16 percent of these cities might witness warming rates 50 to 100 percent higher than their hinterlands under 2°C of warming, a scenario likely within reach by mid to late 21st century.
The implications of these findings are profound, particularly given the demographic importance of medium-sized cities, which collectively outnumber larger urban conglomerations by a factor of more than two and a half. Situated primarily in already warm tropical and subtropical climates, these cities face amplified heat stresses that could significantly impact human health, urban infrastructure, and energy consumption. Elevated temperatures in these urban areas pose heightened risks of heat-related illnesses, strain on healthcare systems, and increased mortality during extreme heat events intensified by climate change.
Dr. Sarah Berk, who led this investigation during her doctoral studies at UEA’s School of Environmental Sciences, emphasizes the dual challenges cities face: increasing regional temperatures coupled with the intensification of urban heat islands. She notes the limitations of global climate models in resolving temperature changes at the scale of medium-sized cities, advocating for complementing these models with machine learning frameworks to capture finer-scale urban warming phenomena. Now based at the University of North Carolina at Chapel Hill, Dr. Berk’s research marks a critical advancement in urban climate science, underscoring the urgency for tailored climate adaptation strategies.
Professor Manoj Joshi of UEA’s Climatic Research Unit, a co-author of the study, highlights that many tropical and subtropical urban areas are projected to exceed the warming rates of their surrounding environments, thereby intensifying urban heat stress. This discrepancy between urban and rural warming is particularly notable in densely populated regions of North-East China and northern India, where certain cities may experience temperature rises of around 3°C, while Earth System Models project only 1.5 to 2°C warming for their rural hinterlands.
The study’s focus on medium-sized cities is a deliberate and crucial aspect, as these urban centers often lack the resources and infrastructure available to megacities to mitigate extreme heat impacts. Enhanced urban warming driven by climate change means that these cities could encounter unprecedented heat challenges that demand urgent, localized mitigation and adaptation measures. The integration of machine learning enables the dissection of complex climate-urban interactions that traditional modeling may overlook, aiding policymakers and urban planners in creating resilient urban environments.
To ensure the robustness of their analysis, the research team deliberately excluded cities located in mountain and coastal areas, where topography, proximity to large water bodies, and other localized factors can confound temperature readings. This methodological rigor ensures that the observed warming trends are strongly attributable to the physical processes related to regional climate and urbanization effects rather than extraneous geographical influences.
Among the five largest urban centers analyzed, Jalandhar in India, Fuyang in China, and Kirkuk in Iraq demonstrate the most pronounced additional warming — about 0.7 to 0.8°C higher compared to their rural surroundings. Conversely, Marrakech in Morocco and Campo Grande in Brazil did not exhibit significant disparities, suggesting variability in urban heat island amplification due to differing regional and urban characteristics. However, other cities like Asyut in Egypt, Patiala in India, and Shangqui in China may experience increases in urban warming ranging from 1.5 to 2°C above rural counterparts, essentially doubling their heat exposure relative to surrounding areas.
This research not only spotlights the pressing reality of enhanced urban heat exposure in tropical and subtropical cities but also underscores the limitations of relying solely on conventional Earth System Models for urban climate projections. By integrating artificial intelligence methodologies with climate science, the study paves the way for more nuanced and actionable climate risk assessments tailored to urban centers around the globe. This integrated approach is vital for understanding and addressing the mounting threats to human health and urban sustainability posed by climate change.
The findings illuminate a clear message: the next generation of urban planning, public health strategies, and climate adaptation policies must incorporate advanced predictive tools such as machine learning to grasp the true extent of urban heat risks. With climate-driven extreme heat events projected to increase in frequency and severity, these insights offer a critical foundation for resilient and climate-sensitive urban development, especially in regions where populations are rapidly urbanizing under already high thermal stress.
Supported by the Natural Environment Research Council and the ARIES Doctoral Training Partnership, and with collaboration extending to researchers now at the Karlsruhe Institute of Technology, this pioneering work elevates our comprehension of urban climate dynamics amid global warming. The study titled “Amplified warming in tropical and subtropical cities at 2°C climate change” was formally published on February 3, 2026, and marks a significant milestone in urban climatology research, offering a clarion call for urgent, informed responses to the emerging urban climate crisis.
Subject of Research: Amplified urban warming and urban heat island intensification in medium-sized tropical and subtropical cities under projected 2°C global warming.
Article Title: Amplified warming in tropical and subtropical cities at 2°C climate change
News Publication Date: 3-Feb-2026
Web References: https://www.pnas.org/doi/10.1073/pnas.2502873123
References: Berk, S., Joshi, M., Nowack, P., & Goodess, C. (2026). Amplified warming in tropical and subtropical cities at 2°C climate change. Proceedings of the National Academy of Sciences.
Keywords: urban heat island, climate change, tropical cities, subtropical cities, machine learning, climate projections, urban warming, medium-sized cities, heat stress, global warming, adaptation, urban climatology
