# Urban heat in global cities and the role of nature-based solutions in mitigating future climate risks

Manuel Esperon-Rodriguez, Rachael V Gallagher, Jonathan Lenoir, Victor L Barradas, Linda J Beaumont, Carlo Calfapietra, Paloma Cariñanos, Stephen J Livesley, Tamara Iungma, Gabriele Manoli, et al.

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## To cite this version:

Manuel Esperon-Rodriguez, Rachael V Gallagher, Jonathan Lenoir, Victor L Barradas, Linda J Beaumont, et al. Urban heat in global cities and the role of nature-based solutions in mitigating future climate risks. Environmental Research: Climate, 2025, 4 (2), pp.023001. 10.1088/2752-5295/adcb61.hal-05352104

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Submitted on 6 Nov 2025

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PERSPECTIVE

Urban heat in global cities and the role of nature-based solutions
in mitigating future climate risks

Manuel Esperon-Rodriguez<sup>1,2,*</sup> <sup>ID</sup>, Rachael V Gallagher<sup>1</sup>, Jonathan Lenoir<sup>3</sup>, Victor L Barradas<sup>4</sup>,
Linda J Beaumont<sup>5</sup>, Carlo Calfapietra<sup>6,7</sup>, Paloma Cariñanos<sup>8,9</sup> <sup>ID</sup>, Stephen J Livesley<sup>10</sup>,
Tamara Iungma<sup>11,12,13</sup> <sup>ID</sup>, Gabriele Manoli<sup>14</sup>, Renee M Marchin<sup>1</sup> <sup>ID</sup>, Timon McPhearson<sup>15,16,17</sup>,
Christian Messier<sup>18,19</sup>, Mark Nieuwenhuijsen<sup>11,12</sup>, Sally A Power<sup>1</sup>, Paul D Rymer<sup>1</sup> and Mark G Tjoelker<sup>1</sup>

E-mail: m.esperon-rodriguez@westernsydney.edu.au

Keywords: climate change, climate risk, climate exposure, global warming, human societies, urban areas, urban forests

Supplementary material for this article is available online

Abstract

Approximately eight billion people are living on Earth today with more than half (55%, ~4.2 billion) living in cities—a proportion predicted to increase to 70% (~6.6 billion) by 2050. As the human population grows, urban residents will face increasingly extreme temperatures under future climate change, which will affect human well-being, health, and mortality. However, nature-based solutions offer promising strategies to mitigate these impacts. Here, we analyst future projections of the maximum temperature of the warmest month, as a proxy for extreme heat exposure across 5646 cities in 218 countries. We show that by mid-century, this climate metric is projected to increase by an average of +1.7 °C (± 0.5 °C), with the largest increases (~4 °C) projected to occur in mid-to-high latitude cities of Europe, North America, and Australia. We highlight the urgent need to adopt nature-based solutions to mitigate projected increases in urban heat and contribute to net-zero CO₂ emissions goals.

<footnote n="1">¹ Hawkesbury Institute for the Environment, Western Sydney University. Locked Bag 1797, Penrith, New South Wales 2751, Australia</footnote>

<footnote n="2">² School of Science, Western Sydney University. Locked Bag 1797, Penrith, NSW 2751, Australia</footnote>

<footnote n="3">³ UMR CNRS 7058 'Ecologie et Dynamique des Systèmes Anthropisés' (EDYSAN), Université de Picardie Jules Verne, Amiens, France</footnote>

<footnote n="4">⁴ Laboratorio de Interacción Planta Atmosfera, Instituto de Ecología, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico</footnote>

<footnote n="5">⁵ School of Natural Sciences, Macquarie University North Ryde, Sydney, New South Wales, Australia</footnote>

<footnote n="6">⁶ Research Institute on Terrestrial Ecosystems (IRET), National Research Council of Italy (CNR), Via G. Marconi 2, Porano, TR 05010, Italy</footnote>

<footnote n="7">⁷ National Biodiversity Future Centre (NBFC), Piazza Marina 61, Palermo 90133, Italy</footnote>

<footnote n="8">⁸ Department of Botany, Faculty of Pharmacy University of Granada, Granada, Spain</footnote>

<footnote n="9">⁹ IISTA-CEAMA, Andalusian Institute for Earth System Research University of Granada, Granada, Spain</footnote>

<footnote n="10">¹⁰ School of Agriculture, Food and Ecosystem Sciences, Faculty of Science, The University of Melbourne, 500 Yarra Boulevard, Richmond, Victoria 3121, Australia</footnote>

<footnote n="11">¹¹ Institute for Global Health (ISGlobal), Barcelona, Spain</footnote>

<footnote n="12">¹² Department of Experimental and Health Sciences, Universitat Pompeu Fabra, Barcelona, Spain</footnote>

<footnote n="13">¹³ CIBER Epidemiología y Salud Pública, Madrid, Spain</footnote>

<footnote n="14">¹⁴ School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland</footnote>

<footnote n="15">¹⁵ Urban Systems Lab, The New School, New York, NY, United States of America</footnote>

<footnote n="16">¹⁶ Cary Institute of Ecosystem Studies, Millbrook, NY, United States of America</footnote>

<footnote n="17">¹⁷ Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden</footnote>

<footnote n="18">¹⁸ Institut des sciences de la forêt tempérée, 58 rue principale, Département des sciences naturelles, Université du Québec en
Ouïaouais, Ripon, Québec J0V 1V0, Canada</footnote>

<footnote n="19">¹⁹ Centre d'étude de la forêt, Département des sciences biologiques, Université du Québec à Montréal, 141 avenue du
Président-Kennedy, Montréal, Québec H2X 1Y4, Canada</footnote>

<footnote>* Author to whom any correspondence should be addressed.</footnote>
# 1. Introduction

Global average temperatures in 2024 were the warmest on record, ~1.34 °C above the 20th-century average (NOAA 2024a). People worldwide are already experiencing record heatwaves, drought and extreme events, and global policy failures have placed the planet on a path toward 2.5 °C–2.9 °C of warming by 2100 (UNEP 2023). Furthermore, in November 2022, humanity reached an unprecedented milestone: the human population surpassed eight billion people worldwide (UN 2022). Recently, scientists published a warning on the need to reduce the rate of human population growth to ensure the long-term sustainability of life on Earth (Lidicker 2020, Crist et al 2022). A trend of note is the increasing urbanization of global populations. Urban areas cover ~3% of the Earth's land surface area (Liu et al 2014), yet 55% of the world's population (~4.2 billion people) lives in urban areas (UN 2018). This proportion is projected to increase to 70% (~6.6 billion people) by 2050, with a corresponding increase in the area of urbanized land of 0.6–1.3 million km² predicted by 2050, an increase of 78%–171% (Huang et al 2019). As the human population grows, cities will continue expanding their ecological footprint and face multiple challenges in return.

Common consequences of rapid urban growth include increasing poverty, income inequality, negative impacts of human health, and displacement and marginalization (Dyer et al 2024b). Many people are unable to equitably access the benefits of urbanized regions (UN-Habitat 2022), which can lead to extreme poverty juxtaposed with extreme wealth. Rapid urbanization also leads to decreased air quality, increased CO₂ emissions and urban heat island (UHI) effects (Shukla and Parikh 1992, Wei and Ewing 2018), where the concentration of heat-absorbing material tends to compound extreme temperature events (Manoli et al 2019). The UHI effect is exacerbated by increasing global temperatures and exposure to lethal heatwaves where trends of increasing heat-related mortality are largely ascribed to climate change (WHO 2023, Dyer et al 2024b).

UHIs occur when air temperature is higher in urban areas than rural surroundings, a difference induced by urban development that increases heat-trapping by built surfaces and human activities (Ferguson and Woodbury 2007). UHI can increase average air temperatures in cities by +1 to +4 °C depending on the spatial extent of the city and its population density (Oke 1973, Manoli et al 2019). In large and densely populated cities, the UHI effect can amplify extreme urban heat. Such is the case of Mexico City (Mexico) and Montreal (Canada), where the UHI can increase temperatures to 10 °C and 12 °C above surrounding rural areas, respectively (Ballinas and Barradas 2016, Shu et al 2023). Heatwaves also exacerbate the UHI effect, although the pattern of the effect is influenced by precipitation and wind (Shu et al 2023). Human thermal stress can be twice as large in cities compared to their surrounding rural areas (Wouters et al 2017). Extreme heat conditions have been responsible for half a million of human deaths globally (Zhao et al 2021b) and are the cause of many serious health issues (Heaviside et al 2017). For example, between 2000 and 2019 across 854 urban areas in Europe, 13 589 deaths were attributed to heat (Masselot et al 2023). Importantly, some segments of human society are more exposed and thus more vulnerable to the impacts of urban heat, including the elderly, babies, and low-income individuals with fewer economic resources to evade extreme heat (Taylor et al 2015, Hsu et al 2021). The UHI effect is exacerbated by the lack of green cover in urban areas. Nature-based solutions (NbS, i.e. actions that protect, restore, and manage ecosystems to address societal challenges; IUCN 2022), such as urban forests, green roofs, and parks, have been highlighted as effective strategies to reduce UHI (Goodwin et al 2023, McPhearson et al 2023). These solutions not only provide cooling through evapotranspiration and shading but also offer additional benefits like improved air quality, biodiversity and human well-being (Keeler et al 2019, Jabbar et al 2022).

The effects of climate change are already being felt in cities globally. For instance, the magnitude and frequency of extreme heat events have increased in metropolitan regions across the world, including recent high-profile extreme heat events in North and South America, India, Europe, and Australia (World-Bank 2020, Philip et al 2022, Adnan et al 2022, Kishore et al 2022, Marengo et al 2022, Debnath et al 2023). In the Midwest, mid-Atlantic and Northeast of the US, 76 million people were under some level of heat alert in June 2024 with temperatures soaring 1 °C–3 °C above average (NOAA 2024b). In India, more than 40 000 suspected heatstroke cases and over 100 deaths were recorded after a prolonged heatwave in summer 2024, where Delhi recorded temperatures of 52.9 °C and 17 other cities hit a maximum of 49 °C (IMD 2024). These are examples of the consequences of mean global land surface air temperatures increasing by about +1.1 °C compared to the pre-industrial baseline (IPCC 2023). However, as temperatures continue to rise, it is expected that extreme weather and climatic events will become more frequent. In their latest report, the Intergovernmental Panel on Climate Change concluded that there is more than a 50% chance that the global temperature rise will exceed +1.5 °C between 2021 and 2040 across several scenarios (IPCC 2023). Innovative strategies are, therefore, urgently needed to address the emerging negative impacts of increasing exposure to extreme heat in urban areas as the climate continues to warm.
Here, we analysed future projections of the maximum temperature of the warmest month (MTWM) across 5646 cities in 218 countries. Following our analysis of projected urban heat increases, we discuss how NbS can address these challenges and highlight an urgent need for coordinated NbS to mitigate projected increases in urban heat.

## 2. Future climate risk in global cities

We analysed future changes in the MTWM. We used MTWM as an indicator of exposure to heat stress—a component of climate vulnerability under the accepted IPCC definition (Field et al 2014). However, while MTWM provides insight into potential maximum temperatures and may reflect more frequent and intense heatwave events, we note that this metric alone does not fully capture the complexity of extreme heat exposure in urban environments. Extreme heat exposure is influenced by various factors, including the persistence of high temperatures over extended periods, which this study does not address. Nonetheless, we followed this approach because extreme heat events and the associated consequences for MTWM can increase the risk of heat-related illnesses and exacerbate existing health conditions (Wong et al 2013, Masselot et al 2023). MTWM was obtained from climate records by identifying the highest value among the monthly maximum temperatures within a given year or longer period, representing the warmest temperature extreme experienced during that time frame (O'Donnell and Ignizio 2012). MTWM can vary in magnitude and month of occurrence between hemispheres and across regions and bioclimatic zones. For example, continental and arid regions often experience higher MTWM compared to coastal or humid regions (Rind 1998).

The future projections of MTWM shown here are based on the median of five global climate models for the period 2041–2070 and the high-emissions scenario of the Shared Socioeconomic Pathways (i.e. SSP5-8.5) (see more details in box 1). Across 5646 cities in 218 countries, we found that all cities will experience increases in MTWM, with an average of +1.7 °C (± 0.5 °C standard deviation; compared to the baseline period 1981–2010) within the next 50 years. There are 88 cities (~2%) expected to experience considerably warmer urban climate extremes of between +3 °C and +4 °C. The highest increases are projected in mid-latitude regions of Australia, Russia, and Ukraine. These geographic patterns are similar to previous global studies, although these studies do not show results explicitly for urban areas. For example, Zhao et al (2021a) used global multi-model projections of urban climates to predict substantial warming by the end of the century in many places globally, including the Middle East, northern Central Asia, north-eastern China, inland South America and Africa, and the United States. Huang et al (2019) found that urban land expansion may result in average increases in summer daytime and night-time warming of up to ~+3 °C in cities worldwide.

In both hemispheres, the smallest increases in MTWM (ΔMTWM compared to baseline conditions during 1981–2010) are projected to occur in the lowest latitudes near the equator. In the Northern Hemisphere, the largest increases occur at mid to high latitudes; whereas the largest increases in the Southern Hemisphere occur in mid to low latitudes (figure 1; supplemental figure S1). While latitude alone was not a predictor of ΔMTWM (table 1), an interactive effect between latitude and hemisphere was observed (table 2). The effect of latitude on ΔMTWM was complex, non-linear and depended on the hemisphere in which the city is located. Towards mid-latitudes, cities in the Southern Hemisphere were likely to have larger future exposure to heat stress. Whereas, in the Northern Hemisphere, exposure to heat stress was greater in cities at higher latitudes.

We found that 1685 cities (~30%) are projected to undergo increases in MTWM between +2 °C and +3 °C, with the largest increases in inland cities in Australia, within Europe—particularly in eastern regions, and North America (figure 1). To a lesser extent, increases in MTWM are also projected in South America and western South Africa. We highlight that these increases do not account for potential strengthening of the UHI effect in the future, as urban population growth and urban land use changes could further amplify this effect increasing exposure to maximum temperatures. Therefore, our estimates of ΔMTWM based on current UHI effects may be conservative, leaving the possibility of greater exposures than we are reporting here, if urban warming intensifies in the future. In addition, the climate data we used here are based on coarse-grained spatial interpolations (1 km²) within urbanized areas and do not account for hotter or cooler microclimates that may occur inside cities. For instance, our analyses cannot explicitly consider locations where microclimatic conditions are more benign or buffered from the exposure to maximum temperatures, providing thermal refugia during extreme heat (e.g. urban green areas or parks with high tree canopy cover) (Ballinas and Barradas 2016, Iungman et al 2023, Calhoun et al 2024).

For many coastal cities, particularly those at low latitudes within the intertropical zone, increases in maximum temperatures are expected to be smaller, on average, likely due to the buffering effects of maritime climates and the cooling effect of sea breezes (Peng and Watanabe 2022). More precisely, we found that
<table><caption>**Table 1.** Summary of generalized additive mixed-effects model (GAMM) results for the effect of three non-correlated variables on the exposure of cities to future change in the maximum temperature of the warmest month (ΔMTWM), namely the absolute value of the latitudinal position (Abs(Latitude)) of the city, baseline MTWM (MTWM<sub>baseline</sub>, average conditions during 1981–2010), and distance of each city to the coast (Dist Coast). We included administrative boundaries (i.e. level 1) from the GADM within each country as a random intercept term in the model formula. Bold indicates a statistically significant effect (*P* < 0.05). Model *R*<sup>2</sup> = 0.84 and deviance explained = 89.3%.</caption><thead><tr><th>Dependent variable</th><th>Explanatory variable</th><th>Coefficient estimate</th><th>Std. Error</th><th>t-value</th><th>P-value</th></tr></thead><tbody><tr><td rowspan="4">ΔMTWM</td><td>Intercept</td><td>0.082</td><td>0.020</td><td>4.072</td><td><strong>&lt; 0.001</strong></td></tr><tr><td>Abs(Latitude)</td><td>0.167</td><td>0.159</td><td>1.049</td><td>0.294</td></tr><tr><td>MTWM<sub>baseline</sub></td><td>0.181</td><td>0.017</td><td>10.336</td><td><strong>&lt; 0.001</strong></td></tr><tr><td>Dist Coast</td><td>0.229</td><td>0.016</td><td>14.546</td><td><strong>&lt; 0.001</strong></td></tr></tbody></table>

<table><caption>**Table 2.** Summary of generalized additive mixed-effects model (GAMM) results showing the estimated degrees of freedom (Edf), reference degrees of freedom (Ref.df), *F*-values, and *P*-values for the model. The explanatory variables include the interaction between absolute latitude (Abs(Latitude)) and hemisphere (NH and SH for Northern Hemisphere and Southern Hemisphere, respectively) as fixed effects and the administrative level (administrative boundaries level 1, see details in Box 1) as a random intercept term. Bold indicates a statistically significant effect (*P* < 0.05).</caption><thead><tr><th>Dependent variable</th><th>Explanatory variable</th><th>Edf</th><th>Ref.df</th><th>F-value</th><th>P-value</th></tr></thead><tbody><tr><td rowspan="4">ΔMTWM</td><td>Abs(Latitude)</td><td>0.67</td><td>0.67</td><td>4.05</td><td>0.101</td></tr><tr><td>Abs(Latitude):NH</td><td>3.36</td><td>3.87</td><td>20.28</td><td><strong>&lt; 0.001</strong></td></tr><tr><td>Abs(Latitude):SH</td><td>8.28</td><td>8.45</td><td>10.59</td><td><strong>&lt; 0.001</strong></td></tr><tr><td>Administrative level</td><td>860.4</td><td>1159</td><td>4.23</td><td><strong>&lt; 0.001</strong></td></tr></tbody></table>

increasing distance of each city from the coast increases ΔMTWM (table 1). When we assessed the effect of the baseline values of MTWM, we found this variable was positively related to ΔMTWM (table 1). Cities with high baseline MTWM tend to experience greater increases in maximum temperatures, potentially indicating a feedback mechanism where already warmer areas are warming even faster.

While cities of North Africa and South Asia are projected to experience relatively small increases in MTWM, these regions are currently already exposed to some of the highest maximum temperatures on Earth (supplemental figure S2). Although people in these regions might be behaviourally and physiologically acclimatized to these temperatures (Xu *et al* 2020), the Middle East and North Africa remain two of the regions most vulnerable to the negative effects of climate change, including heat-related mortality (Hajat *et al* 2023). The projected increases in urban heatwaves may exacerbate the risk of heat exposure, especially at high latitudes in Europe (Zheng *et al* 2021). Additionally, this risk will be intensified by not only an increase in the number of days of extreme heat, but also higher night-time temperatures, both of which have negative impacts on human health and mortality (Amengual *et al* 2014, Royé *et al* 2021). In the US, for example, by the mid-21st century, the annual numbers of days with heat exceeding 37.8 °C and 40.6 °C are projected to double and triple, respectively, compared to the 1971–2000 baseline (Dahl *et al* 2019).

We recognize that although our analysis of MTWM provides valuable insights into potential changes in maximum temperatures, this metric alone does not fully capture the multifaceted nature of extreme heat exposure in urban areas. Factors such as the duration and frequency of high-temperature events, as well as the persistence of temperatures above critical thresholds, precipitation and wind, also play crucial roles in determining heat-related risks (Ebi *et al* 2021, Faurie *et al* 2022). Future studies should consider incorporating additional metrics, such as the number of heatwave days or degree-day sums, to provide a more comprehensive assessment of urban heat exposure. These metrics could offer insights into the persistence of high temperatures and their cumulative effects on urban populations and infrastructure.

The Notre Dame Global Adaptation Initiative (ND-GAIN) is an index of a country's vulnerability to climate change and its capacity for investment in adaptation actions (Chen *et al* 2015). This index relates to adaptive capacity under the IPCC definition of vulnerability (Field *et al* 2014). ND-GAIN measures overall vulnerability by considering six life-supporting sectors: food, water, health, ecosystem services, human habitat, and infrastructure. While readiness is assessed through the three components of economic, governance and social readiness, this readiness index measures a country's ability to leverage investments and undertake adaptation actions (Chen *et al* 2015). After assessing the 2021 Readiness Score quantified by ND-GAIN for all the counties of all the cities assessed here, we found a positive correlation between projected increases in MTWM and country's present-day ND-GAIN scores (Adjusted *R*<sup>2</sup>: 0.12, *F*-statistic: 641.7, *P* < 0.001). This suggests that cities expected to experience greater temperature increases are often located in countries with higher adaptation capacity, particularly in the Northern Hemisphere. While this association is statistically significant, it explains only a small portion of the data variance (12%), indicating that other factors also play important roles in determining adaptive capacity. This finding highlights a
<figure><img src="image_6.png" /></figure>

complex relationship between projected climate impacts and adaptive capacity. While it is encouraging that many cities facing larger temperature increases are in countries with high readiness scores, this does not negate the urgent need for adaptation efforts globally. Moreover, the relatively low explanatory power of our model suggests that adaptive capacity is influenced by many factors beyond national-level readiness scores, such as local initiatives.

Although our analysis does not directly support prioritizing investments based on ND-GAIN scores, it remains crucial to consider equity in global adaptation efforts. Cities in countries with lower ND-GAIN scores, particularly in Africa and South America, may face significant challenges in adapting to climate change due to limited resources and infrastructure. Therefore, we recommend that global capital investments aimed at reducing climate risk and inequality consider both the magnitude of projected climate impacts and the current adaptive capacity of different regions.

We acknowledge, however, that while our climate projections use high-resolution data (~1 km), the ND-GAIN index is only available at the national level. This scale mismatch introduces limitations, as cities within the same country may have vastly different adaptive capacities due to local factors not captured by national-level data. Future research could benefit from developing city-specific readiness indicators to provide a more nuanced understanding of urban adaptation potential. Nonetheless, the absence of globally consistent, city-level adaptive capacity data represents a significant knowledge gap. Developing such datasets
would greatly enhance our ability to assess and compare urban climate vulnerabilities and inform targeted
adaptation strategies. Additionally, we note that while ND-GAIN provides a valuable overview of
national-level adaptive capacity, it may not fully capture city-specific readiness to address urban heat risks,
particularly in the context of NbS. Future research could focus on developing urban-specific readiness
indicators that incorporate factors such as existing green infrastructure, urban planning policies favouring
NbS, and local capacity for implementing and maintaining green spaces.

3. The need for coordinated nature-based solutions

The extreme climate projections presented here highlight the need to implement and expand multiple
solutions, including those focusing on NbS as approaches to climate adaptation. These solutions should aim
to decrease urban heat and contribute towards net-zero CO2 emissions goals, while also providing multiple
co-benefits for addressing other issues associated with urbanization (Frantzeskaki et al 2019, Keeler et al
2019, Iungman et al 2023). However, we recognize that some NbS do not contribute to net-zero CO2, by
primarily serving to prevent greenhouse gas emissions rather than actively removing CO2 from the
atmosphere (e.g. conserving existing wetlands, preserving coral reefs).

The context-specific nature of NbS implementation may vary based on local climate and environmental conditions. Scaling NbS across diverse urban environments presents significant challenges due to variations in local climate, geography, and socio-economic conditions (Chausson et al 2020, Debele et al 2023). Effective implementation of NbS requires tailored strategies that consider specific urban characteristics, including existing green infrastructure, community engagement, and local governance structures (Dorst et al 2019). Moreover, the effectiveness of NbS in mitigating urban heat risks can vary widely depending on the ecological context, the specific environmental stressors faced by each city, and the type and characteristics of NbS (Saaroni et al 2018, Chausson et al 2020, Goodwin et al 2023). Therefore, a one-size-fits-all approach is insufficient; localized assessments and adaptations are essential to ensure that NbS are both relevant and effective in addressing the unique challenges of each urban area.

One of the most broadly NbS used in cities to mitigate urban heat and reduce heat-related mortality is expanding tree cover into areas devoid of vegetation while retaining existing trees (Iungman et al 2023). Urban forests (i.e. all trees and shrubs in a city, present in streets, parks, woodlands, abandoned sites and residential areas Miller et al 2015) can reduce temperatures by evaporative cooling, by intercepting solar energy, and by shading impervious surfaces and buildings (Moss et al 2019, Calhoun et al 2024) that tend to store and radiate heat. Tree canopy density has been identified as the most influential driver of different mechanisms of cooling benefits in cities (Rahman et al 2020). For example, the park cool island effect usually ranges between 1.5 °C–3.5 °C, with the stronger effects associated with larger parks whereas air temperature underneath street trees is typically only about 0.94 °C cooler (Saaroni et al 2018). Research in the Upper Midwest U.S. showed that temperature decrease nonlinearly with increasing canopy cover, with the greatest cooling benefit achieved when canopy cover exceeded 40% (Ziter et al 2019). In Belgium, a study demonstrated that locations with 100% canopy cover had a temperature maxima of 5.5 °C mPET (modified Physiologically Equivalent Temperature, a thermal index based on thermoregulation) lower than treeless locations, and when air temperatures rose to 40 °C, cooling by tree canopies increased to 8.8 °C mPET (Gillerot et al 2024). A recent review showed that tree cover of at least 16% is required to achieve a reduction of 1 °C in urban temperatures (Dyer et al 2024b). Additionally, urban forests provide other benefits, including carbon sequestration, lower energy use and fuel consumption, water regulation and reduced risk of flooding, connection to nature, biodiversity habitat, walkability, improvement of human health and well-being, and social integration (Escobedo et al 2011, Keeler et al 2019). Urban forests are often a sound financial investment (Endreny et al 2017, McDonald et al 2019, De Pauw et al 2023). A study on the value of ecosystem services in five U.S. cities found that every dollar invested in an urban planting returned between $1.37 and $3.09 each year (VCL, -Vibrant Cities Lab- 2018).

Urban forests are, however, vulnerable to climate change and other human-induced stressors. A recent
study assessing the climate risk of urban forests in 164 cities worldwide showed that, at present, nearly half
(80) of these cities have more than 50% of their tree species at risk of warming temperatures and by 2050, the
proportion of cities at risk increases to 91% (149) of those evaluated (Esperon-Rodriguez et al 2022b). In
addition to the impacts of climate change, other stressors, such as increased drought, exotic pests, pathogens
and extreme wind are projected to increase in many regions (Seidl et al 2017). These stressors increase the
risk of tree dieback and mortality in cities. One possible strategy to reduce risks of high tree dieback and
mortality in cities is to establish a diverse portfolio of tree species with varying tolerances to a wide a range of
stressors at the street and neighbourhood levels (Paquette et al 2021), while also considering the inclusion of
new, and sometimes exotic, tree species adapted to both present and future climate conditions for each
region (Esperon-Rodriguez et al 2022a, Esperon-Rodriguez et al 2025). To achieve this aim, testing species
tolerance and filling knowledge gaps in the selection of appropriate species should be a priority. Not only will this help reduce widespread loss of canopy cover if commonly planted tree species fail, but it will also increase the biodiversity of the urban forest and its associated provision of ecosystem services.

Urban forests will not be sufficient to mitigate all the projected increases in urban heat, and in some cases, urban trees are not even a reliable or suitable solution. Thus, additional NbS need to be considered and implemented. Examples include the incorporation of green buildings, walls and roofs as well as blue infrastructure solutions, and other cooling technologies for pavements and roads (Sowińska-Świerkosz and García 2022). For example, using materials with high solar reflectance (albedo) for pavements, roads, and building surfaces can help reflect more sunlight and reduce heat absorption (Ziaemehr et al 2023). Implementing permeable pavements and surfaces can allow water to infiltrate the ground, reducing surface runoff and promoting evaporative cooling (Li et al 2013). This can help lower surface temperatures and mitigate UHIs. Designing urban areas with wind corridors and proper ventilation can facilitate air movement and heat dissipation, helping to reduce urban heat (Hsieh and Huang 2016).

For NbS to be effective, nevertheless, they need to be implemented at the local scale in cities worldwide and tailored to each city's climate risk profile (McPhearson et al 2022). Unfortunately, NbS are often unevenly implemented in cities and are limited in practice (Keeler et al 2019, Goodwin et al 2023). Furthermore, their implementation and equitable sharing of benefits and burdens present several challenges. These challenges can be related to access and inclusion, where some NbS (e.g. urban parks, green roofs, and street trees) are often unevenly distributed within cities, leading to disparities in access to green spaces (Heynen et al 2006). Also, community engagement in the planning and implementation of NbS is often limited, leading to solutions that may not address the specific needs and preferences of all urban residents, particularly marginalized communities (Broussard et al 2008). Maintenance and operational costs of NbS can be a burden for under-resourced communities, cities dealing with severe disinvestment trends, or in regions with severely limited public finances (Chishaleshale et al 2015, Roman et al 2021). Importantly, urban NbS often require coordination across multiple sectors and governance levels, and fragmented governance structures can impede the equitable implementation and management of these solutions (Calliari et al 2022). Furthermore, there may be a lack of policies explicitly aimed at ensuring the equitable distribution of NbS benefits, leading to ad-hoc and potentially inequitable outcomes (Heynen et al 2006).

To move forward, we must face the reality of climate change and the disproportionate impact of extreme heat events on cities and their inhabitants. Cities are the core of global climate change mitigation and strategic low-carbon development (Mi et al 2019). Cities in some regions are leading efforts for climate change adaptation and mitigation, with many committing to frameworks for lowering greenhouse gas emissions and greening of urban areas (Nieuwenhuijsen 2024). Cities can also lead the way in climate change action through policy reform, implementation of scientific research and strategic planning (Rosenzweig et al 2010, Dyer et al 2024a). However, we acknowledge that the strategies proposed here may be insufficient to address the current climate emergency and we advocate using multiple and hybrid approaches to radically transform urban development agendas to adapt to urban heat and the impacts of climate change (McPhearson et al 2021). Hence, we urge the sustainable use and re-use of resources as well as the implementation of new mobility solutions to shift current patterns of personal mobility (Barr 2018). We also recognize and support the positive efforts by community groups that are helping to lead the way in local change (e.g. community gardens and greenways being built in cities), whilst recognizing that these solutions need widespread adoption to deliver benefits at larger scales. Furthermore, we recognize the importance of decarburization policies that are driving climate risk assessments and natural capital investments by businesses seeking to offset emissions. Such investments can and should be considered in cities and peri-urban environments. Policies that require the development of heat resilience and heat risk management strategies, as well as climate-friendly strategies to design and build green environments, are urgently needed for heat mitigation today and into the future.

We recommend that future assessments of urban readiness for heat risks consider incorporating metrics specific to NbS potential, such as existing proportion of urban green cover, its distribution, specific and functional composition, local policies and incentives for green infrastructure implementation, community engagement in urban greening initiatives, and technical capacity for NbS design and maintenance. These factors could provide a more nuanced understanding of a city's readiness to implement NbS as a heat mitigation strategy. Future research should focus on developing localized NbS strategies that are directly informed by city-specific climate projections, ensuring that these solutions effectively address the unique environmental challenges faced by different urban areas. By integrating climate data with community needs and ecological contexts, we can enhance the resilience of urban ecosystems and improve adaptive capacity in the face of increasing heat risk.
## 4. Box 1

### 4.1. Climate and cities data

Baseline (average of climate conditions during the period 1981–2010) and future (average climate conditions for the period 2041–2070) climate data for the MTWM were obtained from CHELSA Version 2.1 (climatologies at high resolution for the earth’s land surface areas; Karger et al 2017, 2021) at 30 arc-second (~1 km) resolution. Future climate projections were downloaded for five global circulation models: (1) GFDL-ESM4; (2) IPSL-CM6A-LR; (3) MPI-ESM1-2-HR; (4) MRI-ESM2-0; and (5) UKESM1-0-LL, and the Shared Socioeconomic Pathway SSP5-8.5. We selected SSP5-8.5 as it represents a high-emissions scenario that aligns with current global emission trajectories (IPCC 2023, Meinshausen et al 2024). This ‘worst-case’ scenario allowed us to illustrate the potential maximum impact of climate change on urban heat, emphasizing the urgency for implementing NbS.

We obtained polygons defining the spatial boundaries of 5646 urban areas as a shapefile (vs. 4.0.1, WGS84; 1:10 million; EPSG:4326) from Natural Earth (retrieved January 2024 from: www.naturalearthdata.com). These data were derived from NASA SRTM Plus elevation data and tailored to register with Natural Earth Vector while urban polygons were derived from MODIS satellite data. For each city, we placed a grid (1 km²) over its area and extracted the values of MTWM at each grid cell using the function ‘exact_extract’ from the exactextractr package (Baston et al 2021). Then, we calculated the average across all grid cells to calculate a single MTWM mean value of each city. Exposure to future change in MTWM (or ΔMTWM) across all cities was calculated as the difference between future climate projections (2041–2070; median across five global circulation models) and baseline climate conditions (1981–2010):

$$ \Delta MTWM = MTWM_{\text{Future}} - MTWM_{\text{Baseline}} $$

### 4.2. Statistical analyses

To explain the observed future increases in MTWM (i.e., the exposure to future change in MTWM or ΔMTWM as the response variable) across 5646 of the world’s cities, we used GAMMs to assess non-linear relationships between the response variable (ΔMTWM) and three non-correlated variables as fixed-effect predictors using the mgcv package (Wood and Wood 2015). The predictor variables were the absolute value of the latitudinal position (i.e. the centroid) of the city, baseline MTWM during 1981–2010 (to account for the potential regression to the mean effect), and the distance of each city to the coast. To calculate the distance to the coast of each city, we downloaded the ocean coastline as a shapefile (vs. 4.10, WGS84; 1:10 million; EPSG:4326) from Natural Earth (retrieved November 2024 from: www.naturalearthdata.com); these data were derived from the World Data Bank. Then, we used the function st_nearestFeature from the sf package (Pebesma 2018) to estimate the Euclidian distance between each city’s centroid and the nearest coast. We also assessed the effect of the interaction between absolute latitude and hemisphere to explore and test for potential differences between hemispheres in the effect of absolute latitudinal position on ΔMTWM. To account for spatial correlation, we included administrative boundaries (i.e. level 1) from the GADM vs. 4.1 (retrieved November 2024 from: https://gadm.org) within each country as a random intercept term in the model formula. All variables were standardized and their units, therefore, were given in units of standard deviation. Model outputs are presented in tables 1 and 2.

Linear regressions were fitted to evaluate the relationship between future increases in MTWM (i.e. the exposure to future change in MTWM or ΔMTWM as the response variable) and the country’s ND-GAIN Readiness Score for 2021 using linear models (i.e. the lm function). Data for the countries’ ND-GAIN Readiness Score for 2021 were obtained from Chen et al (2015). Model performance was evaluated through the calculation of the F-statistic at a significance level of P < 0.05. All analyses were conducted using the statistical software R version 4.2.0 (R Core Team 2022), and maps were generated using maptools package (Bivand et al 2022).

## Data availability statement

The data that support the findings of this study are openly available at the following URL/DOI: https://figshare.com/articles/dataset/_/28355390. Data will be available from 01 May 2025.
Acknowledgments

This study was conducted as part of the ‘World Scientists’ Warning of a Climate Emergency’ (Ripple et al 2020) initiative, led by William J Ripple (Oregon State University, USA; https://scientistswarning.forestry.oregonstate.edu). We sincerely thank William J Ripple for the opportunity to contribute to this initiative. We also extend our gratitude to John Baumgartner for his assistance with data analysis and to Annick St-Denis for her valuable feedback during the manuscript revision process. MER received funding from the Research Theme Program at Western Sydney University.

ORCID iDs

Manuel Esperon-Rodriguez https://orcid.org/0000-0003-3649-2134

Paloma Cariñanos https://orcid.org/0000-0002-8955-2383

Tamara Iungma https://orcid.org/0000-0002-0713-7750

Renee M Marchin https://orcid.org/0000-0002-4154-8924

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