YRD is one of the most densely populated and economically developed regions in China. Driven by rapid population growth and high-speed economic development, the region has experienced extensive urban spatial expansion over recent decades (Shi et al., 2025; Tian et al., 2011). The SuXiChang metropolitan area, consisting of the cities of Suzhou, Wuxi, and Changzhou, is one of the important urban agglomerations within the YRD (Fig. 1a and 1d), located in a subtropical monsoon climate zone. The urbanization process in this region has significantly intensified both the spatial extent and the intensity of UHI (Lu et al., 2018), consequently altering the regional thermal landscape pattern, such as the shrinking of thermal transition zones. As a result, this area is facing severe urban thermal environment risks.

Meteorological observations from automated weather stations, including air temperature, wind speed, and wind direction, were collected and quality-controlled from 2016 to 2020. Stations located within 1 km of large water bodies, or with elevations exceeding 100 m relative to the lowest station, were excluded to ensure environmental representativeness (Yao et al., 2023). Observation stations were further classified into urban and rural categories (Fig. 1b, Table S1), referring to anthropogenic heat flux (AHF) intensity (Fig. 1c) and built-up density (Fig. 1d). Specifically, urban stations (U1–U10) are characterized by densely built-up areas with high AHF intensity (> 5 W m⁻² in a four-kilometer buffer zone); whereas rural reference stations were matched according to the same latitudinal zone as the urban areas, featuring low impervious surface coverage and flat topography. Sensitivity tests using alternative rural reference stations located closer to the coast suggest that the potential bias arising from the limited longitudinal spread of the rural stations is small. In addition, the wind directions selected for analysis should be filtered to avoid interference from large urban-derived heat in rural areas. Due to the aggregated development of urban agglomerations, area and station classifications in this study do not strictly adhere to administrative boundaries.

In addition, PBLH data were obtained from a global continental dataset (Guo et al., 2024), which integrates high-resolution radiosonde measurements, ERA5 reanalysis, and Global Land Data Assimilation System (GLDAS) products through machine learning algorithms. This dataset has a 3-hourly temporal resolution and 0.25°×0.25° horizontal spatial resolution. The mean bias between the dataset and the PBLH recovered from radiosonde observations is -0.9 m, and the root mean square error (RMSE) of the training set and the test set is 243 and 370 m, respectively. The region of interest (ROI) was delineated based on the location of downtown areas of Changzhou, Wuxi, and Suzhou (represented by the U2, U4, and U6 stations, respectively), with a spatial extent of 31–32° N, 119.75–121° E, and covers 86.3 % of the urban meteorological stations included in the analysis (Fig. 1c). Grid data within the ROI were selected and spatially averaged based on prevailing wind direction to represent the PBLH over the core urban agglomeration region.

Wind conditions play an important role in modulating the intensity and spatial heterogeneity of the UHI (He, 2018; Yang et al., 2023). The regional background wind speed and direction were derived from four representative observation stations (Fig. 3). The prevailing wind directions exhibit pronounced seasonal variation, which is partly governed by the East Asian monsoon system. During summer (June–August), southeast flows prevail under the influence of the Western Pacific subtropical high (Shi et al., 2025; Zong et al., 2021), and the urban thermal plumes are primarily driven by solar heating and the sensible heat flux from engineering materials. Conversely, the frequency of northwest winds increases relatively in winter (December–February), driven by the continental Siberian high (Dai et al., 2025), and anthropogenic heat flux is a key driver of thermal plumes (Varentsov et al., 2018). Spring and autumn serve as transition periods, displaying greater variability in wind direction, and may be influenced by local circulations (e.g., sea breezes and lake breezes) under weak synoptic conditions (Yang et al., 2023). It is noteworthy that the prevailing monsoon direction aligns with the arrangement of the urban agglomerations, both being northwest-southeast (e.g., the urban U1–U2–U4–U6–U9 and U3–U5–U7), and there are no strong heat sources in the upstream or downstream of rural areas along this direction (Fig. 1b). Therefore, the characteristics of UHA under northwest and southeast wind conditions are discussed in detail.

The spatiotemporal characteristics of CUHI and UHA under northwest wind conditions are shown in Supplement Fig. S1 and Fig. 4. The regionally daily-averaged CUHII ranges from 0.22 to 1.38 °C and generally shows higher intensity at night than during daytime (Fig. S1). For upstream areas, the daily-averaged UHA intensity is negative (e.g., -0.17 °C in region U1, Fig. 4), indicating a mitigation of the CUHI (e.g., U1 exhibits a daytime urban cold island effect, Fig. S1a). Favourable ventilation conditions help reduce the UHI (Zheng et al., 2022), and increasing wind speed usually leads to a decrease in CUHII (Tian et al., 2023). When the wind speed exceeds a critical threshold, the UHI intensity may approach zero (He, 2018). Along the downwind direction, regional UHA intensity changes from negative to positive and progressively strengthens, reaching up to 0.32 °C in the strongest downstream region (Fig. 4). This spatial gradient indicates that heat from multiple urban sources is transported and superposed downstream through advection. In addition, UHA exhibits a distinct diurnal contrast, being generally greater at night than during the day, with the average intensity reaching approximately 0.6 °C during peak hours in some regions. For regions with positive daily-average UHA intensity (red lines in Fig. 4), the mean nocturnal UHA intensity (∼ 0.4 °C) is 319 % higher than its daytime counterpart (∼ 0.1 °C). In summary, under northwest wind conditions, the regional UHA pattern indicates that upstream areas benefit from ventilation-induced CUHI relief, whereas downstream urban zones experience enhanced heat loading, and UHA intensity shows day–night asymmetry.

The spatiotemporal characteristics of UHA under southeast and northwest wind conditions exhibit both commonalities and differences. Under southeast wind conditions, a pronounced cooling effect is observed in the upwind region, where the minimum averaged UHA intensity reaches -0.8 °C. Similarly, a distinct spatial gradient exists along the prevailing wind vector, characterized by a progressive increase in UHA toward the downwind region, with the maximum daily-averaged intensity reaching 0.09 °C (Fig. 5). Regions exhibiting a positive daily-averaged UHA (U1–U3) also demonstrate diurnal asymmetry: the mean nocturnal UHA is approximately 0.15 °C, whereas the daytime intensity decreases to -0.05 °C. Notably, CUHII also presents a pattern of higher values at nighttime and lower values during daytime (Fig. S2), consistent with the diurnal variation trend of the UHA (red lines in Figs. 4–5), suggesting that the specific contribution of UHA to the urban thermal environment is frequently obscured in conventional CUHI analysis. Overall, although both wind directions facilitate upwind ventilation and downwind heat accumulation, their net thermal effects differ. Northwest winds mainly exacerbate the CUHI across most regions of the study area, whereas southeast winds exert a more extensive mitigating influence due to the cooling effect of sea breezes (Yang et al., 2023). Consequently, the subsequent sections investigate the driving factors of UHA specifically under northwest wind conditions.

UHA intensity is modulated by meteorological conditions. Previous studies have discussed nocturnal UHA in some cities, such as Birmingham (temperate maritime climate) (Bassett et al., 2016) and Lubbock (semi-arid climate) (Danzig et al., 2025), finding that wind speed and direction are important factors influencing UHA. Extending beyond prior nocturnal analyses, this study examines UHA under both daytime and nighttime conditions while incorporating PBLH as an additional parameter. Daytime (typically characterized by unstable boundary layer conditions) was defined as 08:00–19:00 BST, while nighttime (typically characterized by stable boundary layer conditions) spans 20:00–07:00 (Zhang et al., 2023). Screening was conducted according to the following criteria: mean 10 m wind speed < 4 m s⁻¹ and mean PBLH < 1400 m (700 m) for daytime (nighttime) conditions, with 93.13 % (94.48 %) of the samples meeting the requirements. This section discusses the relationships between UHA intensity, wind field, and PBLH under nocturnal and diurnal conditions.

In a stable boundary layer (SBL), the advection term often dominates the surface heat budget (Stull, 1988). Figure 6 presents the nocturnal UHA intensity as a function of wind speed and PBLH across different regions. For the downstream region with a positive mean UHA (red line in Fig. 6), UHA intensity generally increases and then decreases with increasing wind speed and PBLH. At night, in the absence of solar-driven buoyancy flux, mechanical shear generated by wind is the primary source of turbulent kinetic energy (Stull, 1988), and strong shear forces are beneficial to the development of deep SBL (Zilitinkevich et al., 2002). The variation in UHA appears closely linked to boundary layer processes. In a very SBL, characterized by weak winds, low PBLH, intermittent and weak turbulence (Sun et al., 2012; Xue et al., 2025; Zhang et al., 2024b), horizontal heat transport is suppressed and vertical mixing is negligible, limiting the efficiency of UHA transfer. As wind speed and PBLH increase, horizontal advection and turbulence (driven by mechanical shear) strengthen (Mahrt, 2014), and UHA intensity reaches its peak (e.g., in region U7, the mean UHA intensity can reach 0.88 °C in the wind speed range of 2–3 m s⁻¹, see Fig. 6g1). This phenomenon is consistent with observations in Birmingham and Lubbock (Bassett et al., 2016; Danzig et al., 2025). Consequently, the peak impact of nocturnal UHA typically occurs under moderate wind speed conditions, and this pattern may be universal across cities with different climatic backgrounds. When wind speed further increases, and the stable boundary layer transitions to a neutral boundary layer, vertical turbulent exchange intensifies, promoting vertical dilution of urban heat, leading to a decrease in UHA, which may even produce negative values in some regions (e.g., U3–U6; Fig. 6c–f). Furthermore, for upstream regions with negative mean UHA (e.g., U1), UHA intensity decreases with increasing wind speed and PBLH, demonstrating the mitigating effect of ventilation on CUHI. In summary, nocturnal UHA intensity is nonlinearly modulated by wind speed and boundary layer turbulence, with maximum UHA typically occurring under moderate wind and PBLH conditions.

Advection partially contributes to the thermal budget of the convective boundary layer (CBL) (Stull, 1988). Figure 7 illustrates the daytime variation in UHA intensity as a function of wind speed and PBLH. In downwind regions characterized by a positive daytime mean UHA (red lines in Fig. 7), no systematic correlation is observed between wind speed and UHA intensity; overall, UHA intensity was relatively higher under low wind speed conditions. In contrast, UHA exhibits a pronounced negative dependence on PBLH. As PBLH increased, daytime UHA intensity decreased significantly. When PBLH exceeds 800 m, the mean UHA intensity shifts to negative values across all regions, with the lowest intensity at approximately -0.6 °C. In daytime CBL, intense buoyancy-generated turbulence deepens the PBL (Xian et al., 2024) and efficiently redistributes urban heat, which suppresses downstream canopy-level UHA. Therefore, thermally driven turbulent mixing acts as a critical moderating factor for daytime UHA intensity, with peak UHA intensity typically manifesting under low PBLH conditions.

Numerical simulations were employed to investigate UHA characteristics at higher spatial resolution. Sensitivity experiments utilizing modified LULC configurations have become a mature approach for quantifying urban impacts on regional thermal environments (Moustaoui and Georgescu, 2025; Zhao et al., 2021). In the sensitivity experiment, the LULC within the downtown area of Changzhou (U2) was replaced with croplands to isolate approximately the thermal contribution of urbanization to downstream regions (methodology detailed in Sect. 2.2.3), particularly the impact on the downtown areas of Wuxi (U4) and Suzhou (U6). Model performance was evaluated by comparing simulated 2 m air temperature and 10 m wind field of domain 3 against hourly observations from all available meteorological stations within the ROI (Fig. 8). For 2 m air temperature, the RMSE and the Pearson correlation coefficient (R) were 1.80 °C and 0.92 (p<0.001), respectively (Fig. 8a). For 10 m wind field, the RMSE for wind direction and wind speed were 34.77° and 2.62 m s⁻¹, respectively (Fig. 8b). In addition, the RMSE of PBLH between the model and the dataset (Guo et al., 2024) is 262 m. To provide a more comprehensive validation of the model's performance, we calculated additional statistical metrics for the simulation period. For 2 m air temperature, the Mean Absolute Error (MAE) was 1.29 °C, the Mean Bias (MB) was 0.99 °C (indicating a slight warm bias), and the Index of Agreement (IOA) was 0.93. For 10 m wind speed, the MAE was 2.01 m s⁻¹ and the MB was +1.70 m s⁻¹ (reflecting a typical overestimation of wind speed by WRF in urban areas due to simplified canopy drag representations, Yu et al., 2021), with an IOA of 0.71. For the PBLH, the MB compared to the radiosonde-reanalysis merged dataset was -66 m. These quantitative metrics demonstrate that the model captures both the diurnal thermal cycle and the wind-driven transport dynamics with acceptable accuracy, establishing a solid baseline for the sensitivity experiments.

The observation-based UHA intensity was compared with the WRF-simulated horizontal temperature advection. Taking Wuxi (U4), which lies downwind of Changzhou (U2) under the prevailing northwest wind direction, as an example, the observed daily mean CUHII during the simulation period was 2.24 °C (Fig. 9a). The UHA intensity was negative after sunrise, turned positive around midday, and continued to intensify, reaching a maximum of 3.15 °C at night before declining toward the following morning (Fig. 9b). The daily mean UHA intensity was 1.38 °C, indicating that UHA enhances CUHII under the prevailing wind direction. Figure 9c shows the difference in horizontal temperature advection between the control and sensitivity experiments (CTRL minus EXP). This difference also exhibited a broadly similar pattern with higher values at night and lower values during the day, with a nighttime peak of approximately 0.45 K h⁻¹ and a daily mean of 0.18 K h⁻¹ (Fig. 9c). Overall, the UHA intensity and the horizontal temperature advection showed similar diurnal variations.

Figure 10 illustrates the spatial distribution of the 2 m temperature difference between the control and sensitivity simulations (CTRL minus EXP), approximately representing the thermal and dynamic contributions of upstream urbanization in Changzhou to the downstream canopy temperature. During the analysis hours, northwest winds prevailed in domain 3. Influenced by the UHI, Changzhou exhibited a pronounced warming, with positive temperature anomalies of 3.58 and 0.42 °C at 22:00 and 15:00 BST, respectively. This phenomenon arises from the coupled interplay of thermal and dynamic processes. Thermally, urban materials and anthropogenic heat emissions drive a substantial increase in sensible heat flux. Dynamically, the elevated aerodynamic roughness length of the urban canopy suppresses near-surface wind speeds and ventilation (Oke et al., 2017). Both factors together modulate the local positive temperature anomaly and determine its subsequent advection propagation to downstream regions. The simulations show a diurnal variation in the 2 m temperature differences. At night (22:00 BST), lower wind speeds were accompanied by a high UHA intensity but with a limited advective distance, and strong warming was concentrated in the short distance downstream of the city (Fig. 10a). In contrast, daytime conditions (15:00 BST) featured higher wind speeds, which were accompanied by lower UHA intensity but a greater thermal influence distance (Fig. 10b). Under the prevailing northwest winds, the U10 region was also affected by the thermal forcing of Taihu Lake in addition to UHA. During winter, Taihu Lake functions as a daytime heat sink and a nighttime heat source relative to the surrounding environment (Fig. S3). Furthermore, Cosgrove and Berkelhammer (2018) utilized a Lagrangian atmospheric transport model to demonstrate that the Chicago urban thermal plume caused significant heating at 100–200 m above ground level, extending up to 70 km downwind. The sensitivity experiments in this study show that the downstream canopy region was also heated, with the thermal influence beyond 100 km.

Vertical cross-sections of potential temperature and wind vector differences between control and sensitivity experiments were analysed (Fig. 11). The cross-section along a northwest-southeast direction spans approximately 114 km (Fig. 11a), encompassing cities from Changzhou (U2) through Wuxi (U4) to Suzhou (U6). Note that the coordinate rotation was applied to the horizontal wind component u to align it with the cross-section direction, with positive values corresponding to northwest winds. For example, significant negative horizontal wind components within Changzhou's near-surface layer are due to the urban rough surface. At night (22:00 BST), the atmospheric stratification is stable, and updrafts and downdrafts induced by the UHI can be observed over urban areas, particularly in Wuxi (see the CTRL results in Fig. S4b). Thermal plume was suppressed within the near-surface layer, and substantial heat within Changzhou propagated downstream primarily below 100 m, although the PBLH can reach 750 m (Fig. 11b). Following sunrise, solar shortwave radiation enhanced surface heating and buoyancy-driven turbulence (Zhang et al., 2023), elevating PBLH to approximately 1600 m by afternoon (15:00 BST, Fig. S4c). Compared to nighttime conditions, enhanced ventilation and vertical mixing extended thermal influences throughout the entire PBL depth (Fig. 11c), intensifying the BUHI in Wuxi and Suzhou. As the thermal plume propagates, vertical heat redistribution may occur through turbulent mixing in downstream cities. In addition, the updraft in the downstream area is strengthened, and the vertical circulation in Wuxi during the day may be UHA-enhanced UHI circulation (Fig. 11b–c).

The sensitivity experiments quantified UHA contributions from a single upstream city. In reality, taking the downstream city of Suzhou as an example, its UHI is influenced by multiple upstream heat sources, including weak UHA transport from distant Changzhou superimposed upon strong UHA contributions from adjacent Wuxi. When the LULCs in upstream Changzhou and Wuxi are simultaneously replaced with croplands in the sensitivity experiment, enhanced cross-city thermal plume superposition and greater PBLH differences were shown (Fig. S5). These results demonstrate that the intensity and spatial structure of UHA are closely related to diurnal boundary layer evolution.

The CUHII differences of the downstream adjacent city Wuxi (U4) were evaluated between two experimental scenarios. Figures 12a show scatter plots of ΔCUHII and both wind speed and PBLH across different hours. Nighttime conditions exhibited a higher ΔCUHII (with a maximum increase of 0.68 °C), accompanied by lower wind speed and shallower PBLH (Fig. 12a1–a2); while daytime conditions showed a comparatively lower ΔCUHII (with a minimum increase of 0.17 °C), accompanied by higher wind speed and deeper PBLH (Fig. 12a3–a4). The relative increase in CUHII ranged from 8.1 % to 60.4 %, with the large proportional amplifications occurring during the daytime period. Specifically, the average difference between nighttime and daytime is 0.51 °C (14.2 %) and 0.27 °C (18.9 %), respectively. At night, ΔCUHII exhibited a non-linear response to increases in wind speed and PBLH, characterized by an initial amplification followed by a decrease (Fig. 12a1–a2). In contrast, daytime ΔCUHII decreased with increasing PBLH, while its correlation with wind speed remained ambiguous (Fig. 12a3–a4). In summary, these sensitivity experiments provide corroborations for the observational findings, confirming that UHA is regulated by wind speed and PBLH nonlinearly.

UHA also contributed to an increase in BUHII in the downstream city of Wuxi (U4, Fig. 12b1–b4). Specifically, ΔBUHII increased by a maximum of 0.40 °C and a minimum of 0.11 °C, ranging from 4.7 % to 41.7 %. The average difference between nighttime and daytime is 0.23 °C (7.5 %) and 0.23 °C (23.3 %), respectively. It should be noted that uncertainties remain in BUHII due to the schemes of PBL and urban canopy (Zhu and Ooka, 2023). In a supplementary sensitivity test utilizing the YSU PBL scheme (Hong et al., 2006) coupled with the SLUCM urban scheme (Fig. S6), while the absolute change in ΔBUHII remains comparable in magnitude, the relative change of ΔBUHII is larger, and its variation characteristics in response to wind speed and PBLH remain uncertain. Because the numerical simulation covered a limited time period, the potential moderating roles of wind speed and PBLH on BUHI were not further examined.

In addition, the enhancement of downstream UHI (CUHII and BUHII) is primarily contributed to by UHA, though it is not entirely attributable to this mechanism; it is concurrently modulated by other physical processes. For example, advective transport from upstream urban areas can modify local atmospheric conditions (e.g., ambient humidity, stability), which subsequently influence the thermal environment of the downstream city. Furthermore, changes in the underlying surface will also affect local circulation by thermal (e.g., lake-land breeze circulation) and dynamic (e.g., ventilation) effects, which in turn affect wind and temperature in downstream areas. Ultimately, the increase in downstream UHI is a combined result of multiple processes.