Urbanization processes widen global divergence in extreme temperature variability

Процессы урбанизации увеличивают глобальное расхождение в вариативности экстремальных температур
Borong Lin, Renlu Qiao, Fangzheng Li, Tao Wu, Zeyin Chen, Xiang Ao, Xi Meng, Jin Zhao, Z W Wu, Yue Zhang
2026-07-08

aerosolsblue-green spaceextreme temperature variabilityhuman development indexurbanization
Extreme temperature variability (ETV) is a key dimension of climate risk for billions of residents. However, how urbanization shapes global ETV divergence remains unclear. Here, we assemble a 1950-2020 panel of 10,522 cities and demonstrate that ETV trajectories diverge by development status as measured by the human development index (HDI). ETV intensifies in high-development cities, whereas it slowly weakens in low-development cities, resulting in a current gap of roughly 1.66 °C. Decomposing ETV into event frequency and intensity reveals that cumulative ETV is driven mainly by intensity. Using an interpretable machine-learning framework, we find that aerosols are the urban factor most strongly associated with ETV after controlling for climate and geography. Blue-green space is consistently associated with lower ETV, whereas urban morphology has a smaller and context-dependent effect. These findings link global ETV inequality to urban governance and support targeted management that focusses on limiting volatility under climate risk.
1
Between 1950 and 2020, extreme temperature variability (ETV) trajectories diverge by city development status measured by HDI.
2
Blue-green space is consistently associated with lower ETV, while urban morphology has a smaller, context-dependent effect.
3
Decomposition shows cumulative ETV is driven mainly by event intensity rather than frequency.
4
ETV intensifies in high-development (high-HDI) cities but slowly weakens in low-development (low-HDI) cities, producing a current gap of roughly 1.66 °C.
5
Findings link global ETV inequality to urban governance and imply targeted management should focus on limiting temperature volatility.
6
Interpretable machine-learning identifies aerosols as the urban factor most strongly associated with ETV after controlling for climate and geography.

Extreme temperature variability (ETV) trajectories across 10,522 cities worldwide (1950–2020)

How urbanization and urban factors (e.g., aerosols, blue-green space, urban morphology) drive divergence in ETV by development status (HDI), including contributions of event frequency versus intensity

Publication Details
Publication Date
2026-07-08
Journal
Publisher
ISSN
Cited by
0
Access Type
Author Information
Authors
Borong Lin
Renlu Qiao
Fangzheng Li
Tao Wu
Zeyin Chen
Xiang Ao
Xi Meng
Jin Zhao
Z W Wu
Yue Zhang
Explore further
Open the scid.ai AI chat with a ready-made request: it will find papers on a similar topic and help build a literature review.
Find similar papers in the chat
Make a presentation
100%