Understanding the differences in the effect of urbanization on land surface temperature and air temperature in China: insights from heatwave and non-heatwave conditions

Accelerated urbanization and frequent heatwave events pose significant threats to human health. Analyses of the differences in air and land surface temperature (LST) under extreme climates can aid in understanding human-nature ecosystem coupling and the required adaptations to climate change. In this study, we quantified differences in urban and rural temperatures in China under heatwave (CHW) and non-heatwave periods (NHW) conditions and the influence of meteorological factors on these differences. Based on impervious surface data, 2421 urban and rural stations were dynamically classified from 2008 to 2017. Heatwaves were identified using relative thresholds, and differences were explored using meteorological data and MODIS LST data. For LST, urban–rural temperature difference (U-RTempdiff) was highest during the day, whereas air temperature peaks occurred at night, under both NHW and CHW conditions. During CHWs, the daytime U-RTempdiff was greater for LST than for air temperature, reaching 4.24 ± 3.38 °C. At night, U-RTempdiff was slightly lower (1.04 ± 1.41 °C). The proportion of air U-RTempdiff contributed by rural air temperature was significantly higher during CHW nights than during NHW nights, whereas the proportion of land surface and air U-RTempdiff remained relatively stable during daytime. Spatially, the daytime temperature difference in the north decreased with latitude, whereas the difference in the south was lower. Under CHWs, urbanization had a stronger effect on LST than on air temperature, with a slightly smaller difference (0.01 °C yr−1) during the day and a slightly larger difference (0.03 °C yr−1) at night. The contribution of urbanization to LST was higher than that to air temperature, particularly during the day (16.34%). The effects of wind speed and precipitation on the average air urban–rural temperature difference was greater than those of LST under CHW, accounting for 16.13%, with the effects of wind speed being more significant. These results show that a comprehensive perspective is needed to understand the risks associated with a temperature rise risk under extreme climate conditions and to formulate effective mitigation measures that will they improve human thermal comfort under climate change.


Introduction
Intensified urbanization and a growing urban population have amplified urban heat island effects, posing environmental security concerns (Chen et al 2023, Qiao et al 2023a).Concurrently, global climate change has increased the frequency of extreme heatwaves, worsening urban conditions and reducing thermal comfort (Ren et al 2022, He et al 2023).Thus, understanding human-nature interactions in cities is vital to informing proactive climate adaptation strategies.
Clarifying disparities in the urban-rural temperature difference (U-R Tempdiff ) and in urbanization effects under heatwave (CHW) and non-heatwave (NHW) conditions is crucial (Phelan et al 2015, Marx et al 2021).Previous studies considered both land surface temperature (LST) and air temperature, and consistently indicated that heatwaves exacerbate the air U-R Tempdiff in regions such as Dijon (Richard et al 2021), Athens (Founda et al 2015), Oklahoma (Basara et al 2010), Beijing-Shanghai-Guangzhou (Jiang et al 2019), and eastern cities in China (Wang et al 2017a).Advanced remote sensing data revealed similar trends in Iranian cities (Keikhosravi 2019), Cluj-Napoca (Herbel et al 2018), Andalusian cities (García 2022), western North America (Cotlier and Jimenez 2022), and East China (Miao et al 2022).In summary, numerous studies from various locations consistently found increased land surface and air U-R Tempdiff during CHW.
Radiation and convection cause heat exchange between the Earth's surface and the atmosphere, which leads to a close connection between LST and air temperatures, although their characteristics are distinct (Jin and Dickinson 2010, Zhu et al 2013, Good 2016).LST is obtained from remote sensing data linked to land cover, indirectly affects human comfort by influencing outdoor radiative heat, but is sensitive to cloud cover (Li et al 2013, Kong et al 2020, She et al 2022).Air temperature, which is measured from meteorological stations, can be determined more precisely and directly impacts human perception and comfort, although measurements are limited by station distribution (Founda et al 2015, Bian et al 2017, Venter et al 2021).Studies of both LST and air temperature have shown differences in land surface and air U-R Tempdiff , including temporal and spatial variation, as well as differences in the influencing meteorological factors.Typically, land surface U-R Tempdiff is more pronounced than air U-R Tempdiff , particularly during summer and daytime, as observed in cities such as Milan, Beijing, Shanghai, and Mexico City (Cui and De Foy 2012, Anniballe et al 2014, Sun et al 2015, Hu et al 2019).Meteorological factors such as wind speed and direction, precipitation, and local climate affect U-R Tempdiff variation.During heatwaves, reduced wind speed and tall buildings intensify the air U-R Tempdiff (Zong et al 2021).Consecutive clear days cause soil moisture depletion, heat accumulation, and increased air temperature during heatwaves (Miralles et al 2014).Studies on LST have demonstrated its association with reduced wind speed during heatwaves, which increases the land surface U-R Tempdiff (García 2022).Precipitation significantly influences the changes in U-R Tempdiff , whereas wind speed has a smaller impact (She et al 2022).Meteorological factors have varying degrees and scopes of influence on LST and air temperatures, which are attributed to the differences in sensible heat flux and latent heat flux from an energy balance perspective (Ban-Weiss et al 2011, Liu et al 2012).Therefore, considering both LST and air temperature can enhance the understanding of their similarities and differences (Sheng et al 2017, Du et al 2021).
Although past studies have analyzed the impact of urbanization on temperature variables (Wang et al 2017b, Du et al 2021), they have not explored extreme climate conditions, such that knowledge gaps remain.For example, prior studies focused largely on small scales, e.g.cities and local regions (Cui andDe Foy 2012, Sun et al 2015), mainly ignoring national-level perspectives.These studies also primarily examined heatwave effects on LST or air temperature separately, such that comparative studies are lacking.Therefore, national-scale variation in U-R Tempdiff during heatwaves for both LST and air temperatures remain to be investigated.
This study examined LST and air temperature differences, urbanization effects, and the impact of wind speed and precipitation under CHW and NHW conditions.The results provide insights into human-land interactions in urban areas under extreme climates, offering a scientific basis for risk reduction and future urban development.

Study area
This study was conducted on mainland China.The diverse climates, rapid urbanization, and growing urban population of China offer an ideal setting to explore temperature differences and urbanization effects during extreme temperature events across various climates (figure 1).

Data
Meteorological data were obtained from daily observations of 2421 meteorological stations of the China Meteorological Administration (http://data.cma.cn/data/cdcdetail/dataCode/A.0012.0001.html).The dataset included station information (longitude, latitude, and elevation), observation dates, and meteorological variables (temperature, precipitation, and wind speed) from 1978 to 2017.Stations with missing data for any observation variable exceeding 30 d were excluded; interpolation was performed for stations with fewer missing days.LST data were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) composite LST product (MYD11A2) (https://lpdaac.usgs.gov/products/myd11a2v061) for the period 2008-2017.The original data were subjected to preprocessing, including projection transformation, format conversion, and quality control, with an accuracy of 1 K and spatial resolution of 1 km.After processing, data from 2148 stations were used in the analysis.Impervious surface data for 1978-2017 at a resolution of 30 m were obtained from the national impervious surface dataset of China (http://data.starcloud.pcl.ac.cn/zh/ resource/12), with an overall accuracy exceeding 90% (Gong et al 2019).For data stability and consistency, the period 1978-2007 was used as the heatwave reference period and data from the period 2008-2017 were analyzed for LST and air temperature differences.China was divided into seven zones for this study (Huang 1958).

Dynamic classification of urban and rural stations
Dynamic classification of the stations was performed using impervious surface data.A circular buffer with a r 3 km radius was created around each meteorological station (Qiao et al 2023b).If the proportion of impervious surfaces (ISP) within the buffer exceeded 20%, the station was classified as urban; otherwise, it was classified as rural (Homer et al 2004).
Rural stations with an elevation difference exceeding 500 m were excluded.For 2008, we included 783 urban stations and 1365 rural stations; by 2017, there were 1 297 urban stations and 851 rural stations (figure 1).

Definition of heatwaves
In this study, heatwaves were defined as periods from May to September when the maximum temperature exceeded a threshold for three or more consecutive days (Yang et al 2019, Xie et al 2020).The threshold was individually determined for each station based on a dataset of 450 (30 × 15 d) from 1978 to 2007, using a 15 day moving window (7 d before and after the current day); the 90th percentile of the dataset was used as the threshold for specific dates and stations (Liao et al 2018).Therefore, each station had its own daily threshold range.
Typical urban and rural stations were selected based on their yearly ISP, with urban stations having the highest and rural stations the lowest ISP.If multiple stations had the lowest ISP, then the station farthest from the urban station was selected.The selected stations were screened based on their heatwave events.A CHW period was defined when both urban and rural stations encountered a heatwave, and an NHW period was defined when no heatwave occurred.This study focused on cities where the maximum interval without a heatwave event was 1 year.A total of 93 cities were selected for further analysis (table S1).

Effect on and urbanization to temperature
Differences between urban and rural areas under CHW and NHW events were calculated using daily average temperature (T mean ), daily maximum temperature (T max ) and daily minimum temperature (T min ).T max and T min partly represent the daytime and nighttime temperatures, respectively.
The linear trends of urban (T u ) and rural (T r ) stations from 2008 to 2017 were calculated according to Ren and Zhou (2014).The difference between the two trends was defined as the urbanization effect, expressed as follows: (1) The contribution of urbanization (UC) was also calculated as described by Ren and Zhou (2014): If the calculated result exceeded 100%, then it was capped at 100%.To test the significance of the urbanization effect, we applied the Mann-Kendall test with a significance threshold of 0.10 (Liu et al 2014).

Analytical methods
Due to the absolute differences between LST and air temperatures, we compared their differences in terms of percentages, based on the ratio of U-R Tempdiff vs. T, where T represents the rural temperature during the corresponding period of U-R Tempdiff .
In calculations of the effects of wind speed and precipitation changes on LST and air temperature variation, partial correlation and multi-correlation coefficients were calculated and significance tests were performed.Please see the supplementary information for more details.

Correlation between LST and air temperature
Figure 2 shows the correlation between LST and air temperature under NHW conditions, highlighting the urban area, rural area, and U-R Tempdiff .The correlation coefficients between LST and air temperatures exceeded 0.9 for both urban and rural areas, and the correlation coefficient for the U-R Tempdiff was 0.62 (p < 0.01).This result indicated a strong correlation between LST and air temperature.

Spatial and temporal characteristics of land surface and air U-R Tempdiff
Figure 3 shows the spatiotemporal heterogeneity of the land surface U-R Tempdiff .The average, daytime, and nighttime land surface U-R Tempdiff values under CHW (NHW) conditions were 3.66 ± 2.40 • C (3.33 ± 2.04 • C), 5.02 ± 3.66 • C (4.63 ± 2.97 • C), and 2.31 ± 1.97 • C (2.03 ± 1.82 • C), respectively.U-R Tempdiff increased significantly under CHW conditions, especially during the day.Across different climate zones, U-R Tempdiff values were consistently higher during the day than at night (CHW: 0.53 • C-4.17 • C; NHW: 0.77 • C-3.96 • C).The U-R Tempdiff between day and night was more prominent under CHW conditions, except for the NS and CS zones.

Simultaneous investigation of land surface and air U-R Tempdiff
The land surface U-R Tempdiff was higher than air U-R Tempdiff under both CHW and NHW conditions; the differences were more significant during the day (figure 4).Land surface U-R Tempdiff was higher than air U-R Tempdiff during the day for both CHW and NHW, with values of 4.24 ± 3.38 • C and 3.98 ± 2.76 • C, respectively.However, at night, the land surface-air U-R Tempdiff under CHW was slightly smaller than that under NHW (1.04 ± 1.41 Spatially, the differences in land surface and air U-R Tempdiff in the northern regions (CT, MT, and WT) decreased with decreasing latitude during the day.The difference was largest in CT areas (CHW: 4.90 ± 3.47 • C; NHW: 4.03 ± 2.41 • C) and smallest in WT (CHW: 2.98 ± 3.21 • C; NHW: 2.45 ± 2.17 • C).In the southern regions (NS, CS, and SS), the differences were relatively consistent, with minor variations across different periods, ranging from 4.79 ± 2.61 • C to 5.09 ± 2.71 • C (4.76 ± 2.44 • C to 4.89 ± 2.70 • C) during CHW (NHW).Nighttime differences were relatively small across climate zones and those between land surface and air U-R Tempdiff were smaller under CHW than under NHW, except in the CT zone, which contrasted with the daytime pattern.The smallest and largest differences were recorded in the NS (CHW: 0.15 ± 1.15 • C; NHW: 0.34 ± 0.88 • C) and SS zones (CHW: 1.34 ± 0.92 • C; NHW: 1.44 ± 1.39 • C), respectively.
The contribution of the air U-R Tempdiff to rural air temperatures was significantly larger under CHW than at night under NHW, whereas the proportions of land surface and air U-R Tempdiff were relatively stable during the day.The daytime land surface U-R Tempdiff  accounted for 16.72% (CHW) and 16.42% (NHW) of the rural LST, whereas the daytime air U-R Tempdiff accounted for only 3.03% (CHW) and 3.08% (NHW) of the rural air temperature.Spatially, except in the PZ zone, the ratio of the land surface-air U-R Tempdiff under CHW conditions was slightly higher in northern regions (1.77%-3.01%)and slightly lower in southern regions (-0.53% to −1.03%).At night, land surface U-R Tempdiff accounted for 14.63% (CHW) and 13.05% (NHW), and air U-R Tempdiff accounted for 10.11% (CHW) and 8.44% (NHW).Spatially, except for the CT zone, where the ratio of the land surfaceair U-R Tempdiff increased (0.43%) during CHW, the differences in the various climate zones decreased (-0.86% to −5.26%), which further explained the lower nighttime land surface-air U-R Tempdiff observed under CHW than under NHW.

Land surface urbanization effects
There was a larger increase in the urbanization effect at night under CHW than under NHW, whereas the effect decreased slightly during the day (figure 5).The average, daytime and nighttime land surface urbanization effects under CHW (NHW) conditions  were 0.09 • C yr −1 (0.08 • C yr −1 ), 0.06 • C yr −1 (0.09 • C yr −1 ), and 0.13 • C yr −1 (0.08 • C yr −1 ), respectively.Spatially, variation in the urbanization effect across different climate zones was greater during the day, particularly under CHW.
Figure 5 also shows that the contribution of urbanization to urban warming was lower under CHW than under NHW.Overall, the contributions of urbanization to average, daytime, and nighttime warming under CHW (NHW) were 62.49% (75.46%), 70.51% (79.24%), and 64.41% (74.06%), respectively.The contribution of urbanization to warming exhibited a spatiotemporal heterogeneity that was more pronounced under CHW, when the contribution increased with decreasing latitude in both northern and southern regions.
There are several explanations for the smaller contribution of urbanization under CHW than under NHW.Equation ( 2) implies faster temperature growth rate in urban areas than in rural areas under CHW.For example, in Chengdu (CS), the temperature growth rate under CHW (0.22 • C yr −1 ) was larger in urban areas than in rural areas (0.16 • C yr −1 ), indicating a synergistic effect between heatwave conditions and urbanization that accelerated the increase in urban temperature.Conversely, slower temperature growth rate was exhibited in urban areas under CHW.For example, in Chengde (WT), the temperature growth rate under CHW (0.15 • C yr −1 ) was lower in urban areas than in rural areas (0.32 • C yr −1 ).

Spatial and temporal characteristics of air urbanization effects
Similar to the land surface urbanization effect, heatwaves enhanced the air urbanization effect (figure 5).The average, daytime and nighttime air urbanization effects under CHW (NHW) were 0.07 • C yr −1 (0.06 • C yr −1 ), 0.05 • C yr −1 (0.04 • C yr −1 ), and 0.09 • C yr −1 (0.07 • C yr −1 ), respectively.Spatially, unlike the urbanization effect on LST, that on air temperature exhibited greater variation at night than during the day across the different climate zones, especially under CHW.
Similar to the above LST findings, the contribution of urbanization was lower under CHW than under NHW.The average, daytime, and nighttime urbanization contributions under CHW and NHW was 51.91% (82.44%), 54.16% (67.67%), and 57.11% (81.63%), respectively.Spatially, the patterns observed for daytime urbanization contributions to air temperature under CHW aligned with those to LST.However, the nighttime contribution of urbanization was lower in southern regions than in northern regions.
The lower contribution of urbanization under CHW than under NHW may be explained in a similar manner to the above LST findings.However, the specific factors contributing to this difference within the same city may vary.For example, in Chengdu, the temperature growth rate was lower in urban areas (0.17 • C yr −1 ) than in rural areas (0.19 • C yr −1 ), whereas the opposite trend was observed in Chengde (urban: 0.31 • C yr −1 vs. rural: 0.30 • C yr −1 ).

Simultaneous investigation of land surface and air urbanization effects
Under NHW, the land surface urbanization contribution was higher than the air urbanization contribution (daytime: 0.05 • C yr −1 ; nighttime: 0.01 • C yr −1 ) (figure 6), whereas under CHW, this difference decreased slightly during the day (0.01 • C yr −1 ) and increased at night (0.03 • C yr −1 ).
Spatially, differences among climate zones were more significant during the day than at night.Under NHW, the smallest difference in the land surface and air urbanization effects was observed in CT (-0.05 • C yr −1 ) and the largest difference was observed in SS (0.13 • C yr −1 ).However, this trend was less apparent under CHW conditions.At night, the differences among climate zones were relatively small except in PZ, where they ranged from 0.01 to 0.05 • C yr −1 (-0.02 to 0.09 • C yr −1 ) during CHW (NHW).
During the day, the land surface urbanization contribution was higher than the air urbanization contribution (CHW: 16.34%; NHW: 11.58%).Under CHW, the nighttime contribution of land surface urbanization was higher than that of air urbanization (7.30%), and the opposite trend was observed under NHW (-7.47%).Spatially, the pattern was not significant during the day under CHW, whereas under NHW it generally followed the trend of the land surface-air urbanization contribution difference, gradually increasing with decreasing latitude (2.06% in CT to −32.76% in SS).At night, the trend was a slow reduction in the difference in the land surface-air urbanization contribution with decreasing latitude, reaching 0 and then increasing, with a range from -26.57% to -28.08% (-43.30% to -23.56%) from CT to SS areas during CHW (NHW).

Differences in meteorological effects on LST and air temperature
Meteorological factors had a stronger influence on air temperature than on LST, with average increases of 16.13% and 23.66% under CHW and NHW, respectively (figure 7).Wind speed had a more pronounced effect than precipitation.The influence of meteorological factors was also more significant under NHW than under CHW, with increases of 20.43% and   S2 for detailed description of the specific classification criteria.
12.90% in air temperature and LST, respectively.The impact of wind speed on air temperature was significantly higher than that of precipitation (CHW: 76.0%; NHW: 43.18%).The influence of precipitation on the LST increased slightly under CHW (9.09%).Thus, meteorological factors had differential driving effects on LST and air temperature.The study showed that CHW conditions lead to an expansion of both land surface and air U-R Tempdiff .Specifically, the land surface U-R Tempdiff undergoes more significant expansion during the day (CHW: 5.02 ± 3.66 Clear skies and intensified warm air advection worsen heatwaves (Miralles et al 2014), leading to increased solar radiation absorption by highly impervious surfaces such as buildings and roads, thereby amplifying the land surface U-R Tempdiff under CHW conditions (Li et al 2015).Similarly, the air U-R Tempdiff becomes more significant during heatwaves.The increased heat released from urban areas is accompanied by heat emissions from human activities such as air conditioning use (Li andBou-Zeid 2013, Zhao et al 2018).The findings indicated that under CHW, urbanization has a more substantial impact on the LST than does urban air warming.Both land surface and air U-R Tempdiff increase under CHW, but the contributing factors vary, resulting in potentially different magnitudes and impact ranges.

Discussion
The analysis of meteorological driving factors indicated that wind speed and precipitation had a greater influence on air temperature than on LST, especially under NHW conditions.This result is likely due to the increased atmospheric instability and the influence of other meteorological factors, which have greater temperature effect under CHW, whereas under NHW conditions factors such as wind speed and precipitation become more prominent due to relatively stable atmospheric conditions (Wehrli et al 2019, Wang et al 2022).In the study, wind speed had a smaller effect on LST than on air temperature, consistent with previous findings (She et al 2022).Under CHW, reduced precipitation directly affects soil moisture, resulting in the increased release of nighttime sensible heat flux.A lower wind speed alters warm air circulation and impacts surface radiative cooling (Li and Bou-Zeid 2013).Thus, wind speed and precipitation influence LST and air temperatures through distinct mechanisms; however, both influence the U-R Tempdiff , and thus human thermal comfort.
Addressing the divergent temperature trends under CHW conditions requires effective strategies.

Limitations and future directions
This study used air temperature to identify heatwaves, although using LST could yield different results.However, the robust positive correlation between LST and air temperature justified the use of air temperature.Additionally, other meteorological factors may also affect U-R Tempdiff (Su and Dong 2019, He et al 2020b, Wu et al 2021).These factors should be explored in further studies to better understand the mechanisms underlying LST and air temperature disparities.
In previous studies, air temperature was estimated using LST to overcome the limitations of remote sensing studies based on correlations and machine learning algorithms, for example, Prihodko andGoward (1997), Vancutsem et al (2010), Zhu et al (2013) and Chen et al (2022).These types of methods usually perform worse under extreme weather conditions.This study provides additional data support for spatial air temperature estimation under extreme conditions.

Conclusion
This study explored U-R Tempdiff and urbanization effects from the perspectives of LST and air temperature under extreme climate conditions, as well as temperature variation in response to the driving effects of wind speed and precipitation.The results showed that under CHW, land surface U-R Tempdiff was higher than air U-R Tempdiff (daytime: 4.24 ± 3.38 • C, nighttime: 1.04 ± 1.41 • C).The difference was amplified during the day but reduced at night compared to NHW, because the proportion of air U-R Tempdiff in rural air temperatures increased significantly during CHW nights compared to NHW nights, whereas the proportions of land surface and air U-R Tempdiff were otherwise relatively stable.Under CHW, the land surface-air urbanization effects increased (daytime: 0.01 • C yr −1 , nighttime: 0.05 • C yr −1 ).The urbanization contribution showed a similar pattern (daytime: 16.34%; nighttime: 7.30%).Correlation analysis indicated that wind speed and precipitation more strongly influenced air temperature than LST, with an average increase of 16.13% during CHW.Wind speed had a more pronounced impact than precipitation.These findings highlight variability in the influence of meteorological factors on land surface and air U-R Tempdiff during CHW and NHW, offering insights for human adaptation to extreme climate change.

Figure 1 .
Figure 1.Spatial distribution of urban (red color) and rural (blue color) stations in 2017.* Climate zones including SS: South Subtropical Zone; CS: Central Subtropical Zone; NS: North subtropical zone; WT: Warm Temperate zone; CT: Cold Temperate Zone; MT: Middle Temperate Zone; PZ: Plateau Zone.

Figure 5 .
Figure 5. Spatial distribution of land surface and air urbanization effects and their contributions in different periods.(a), (c), (e) land surface urbanization effects and contribution during CHW (average, daytime, nighttime); (b), (d), (f) land surface urbanization effects and contribution during NHW; (g), (i), (k) air urbanization effects and contribution during CHW; (h), (j), (l) air urbanization effects and contribution during NHW.

Figure 7 .
Figure 7.Comparison of meteorological factors on average land surface and air U-R Tempdiff under CHW and NHW conditions.* W: driven by wind speed mainly; P: driven by precipitation mainly; [W + P] + : driven by wind speed and precipitation strongly; [W + P] − : driven by wind speed and precipitation weakly; NC: driven by non-meteorological factors.Please see the tableS2for detailed description of the specific classification criteria.
Measures such as improved ventilation (Qiao et al 2017, He et al 2020a, Yang et al 2021) and increased urban greenery (Top et al 2020, Shreevastava et al 2021) can effectively mitigate the buildup of heat in urban areas, slowing the rise in air temperature.Additionally, implementing green roofs (Sun et al 2016, Wang et al 2020) and white roofs (Li et al 2014) can curb heat buildup at the surface and alleviate LST increases.Given the increasing frequency of extreme climate events, it is vital to develop appropriate adaptation and mitigation strategies for urban renewal (Qiao et al 2020).

4.1. Difference and application of LST and air temperature
Heatwaves tend to expand the land surface U-R Tempdiff , with more significant impacts during the day in coastal cities and at night in inland cities, as demonstrated in Spain (García 2022).Similarly, studies in Beijing (Li et al 2015, Zong et al 2021) and Guangzhou (Luo et al 2023) showed that heatwaves amplify the air U-R Tempdiff , particularly at night.However, other studies suggested that heatwaves do not exacerbate U-R Tempdiff (Chew et al 2021).
Tempdiff was greater than the air U-R Tempdiff (3.98 ± 2.76 • C) during the day, whereas this difference was smaller at night (1.13 ± 1.16 • C).These findings align with reports for Milan, Beijing, and Mexico City (Cui and De Foy 2012, Anniballe et al 2014, Sun et al 2015).Due to variations in the timing and intensity of CHW impacts on land surface and air U-R Tempdiff , the daytime land surface-air U-R Tempdiff was more prominent (4.24 ± 3.38 • C), with the difference decreasing slightly at night (1.04 ± 1.41 • C).As determined in multiple studies, during the day, urban areas with more impervious surfaces and exposed soil absorb and hold solar radiation, causing LST to rise faster (Wang et al 2017a, García 2022, Cheng et al 2023).At night, the absorbed heat is released into the air, leading to a notable increase in air temperature (He et al 2021, Shreevastava et al 2021).
• C, NHW: 4.63 ± 2.97 • C), and the air U-R Tempdiff undergoes more significant expansion at night (CHW: 1.27 ± 2.15 • C, NHW: 0.90 ± 1.51 • C).Under NHW, the land surface U-R Tong He  https://orcid.org/0000-0002-5667-9417Zhi Qiao  https://orcid.org/0000-0002-8971-4952Dongrui Han  https://orcid.org/0000-0002-6206-3918Basara H G, Illston B G and Crawford K C 2010 The Impact of the Urban Heat Island during an Intense Heat Wave in Oklahoma City Adv.Meteorol.2010 1-10 Bian T, Ren G and Yue Y 2017 Effect of urbanization on land-surface temperature at an urban climate station in North China Bound.-LayerMeteorol.165 553-67 Chen S, Yang Y, Deng F, Zhang Y, Liu D, Liu C and Gao Z 2022 A high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations Atmos.Meas.Tech.15 735-56 Chen Y, Yang J, Yu W, Ren J, Xiao X and Xia J C 2023 Relationship between urban spatial form and seasonal land surface temperature under different grid scales Sustain.Cities Soc.89 104374 Cheng Q, Jin H and Ren Y 2023 Compound daytime and nighttime heatwaves for air and surface temperature based on relative and absolute threshold dynamic classified in Southwest China, 1980-2019 Sustain.Cities Soc.91 104433 Chew L W, Liu X, Li X-X and Norford L K 2021 Interaction between heat wave and urban heat island: a case study in a tropical coastal city, Singapore Atmos.Res.247 105134 Cotlier G I and Jimenez J C 2022 The extreme heat wave over Western North America in 2021: an assessment by means of land surface temperature Remote Sens. 14 561 Cui Y Y and De Foy B 2012 Seasonal variations of the urban heat island at the surface and the near-surface and reductions due to urban vegetation in Mexico City J. Appl.Meteorol.Climatol.51 855-68 Du H et al 2021 Simultaneous investigation of surface and canopy urban heat islands over global cities ISPRS J. Photogramm.Remote Sens. 181 67-83 Founda D, Pierros F, Petrakis M and Zerefos C 2015 Interdecadal variations and trends of the urban heat island in Athens (Greece) and its response to heat waves Atmos.Res.161-162 1-13 García D H 2022 Analysis of urban heat island and heat waves using sentinel-3 images: a study of Andalusian cities in Spain Earth Syst.Environ.6 199-219 Gong P, Li X and Zhang W 2019 40-year (1978-2017) human settlement changes in China reflected by impervious surfaces from satellite remote sensing Sci.Bull.64 756-63 Good E J 2016 An in situ-based analysis of the relationship between land surface "skin" and screen-level air temperatures: land skin-air temperature relationship J. Geophys.Res.Atmos.121 8801-19 He B-J, Ding L and Prasad D 2020a Urban ventilation and its potential for local warming mitigation: a field experiment in an open low-rise gridiron precinct Sustain.Cities Soc.55 102028 He B-J, Wang J, Liu H and Ulpiani G 2021 Localized synergies between heat waves and urban heat islands: implications on human thermal comfort and urban heat management Environ.Res.193 110584 He T et al 2023 Anthropogenic activities change population heat exposure much more than natural factors and land use