Urban Air Temperature Research Articles

Case Study: Impact Analysis of Roof-Top Green Infrastructure on Urban System Sustainability in San José, CA

This paper presents results from a case study focusing on analyzing impacts of Green Infrastructure (GI) on sensible and latent heat fluxes, urban microclimate and the subsequent water–energy nexus components of an urban infrastructure system. The case study, focusing on the campus of a public university in San José, CA, aimed to quantify the pre- and post-conditions for a hypothetical GI implementation, which is in support of San José State University’s (SJSU) robust sustainability initiatives, which are also aligned with Silicon Valley’s broader strategic goals. The results revealed that a reduction of 0.3 °C in the average daily peak maximum temperature on campus could be achieved by the GI implementation. Air-conditioning related energy use was projected to decrease by 1.28%, monthly water use by 7052 m3, and it would result in an estimated reduction of approximately 2800 kWh in the water–energy nexus. In addition to lowering the campus’s carbon footprint, GI therefore offers significant economic and environmental benefits in terms of reductions in the urban air temperature, energy usage and water demand. This study provides valuable information for policy makers and low impact development water infrastructure managers considering GI implementation.

Nonlinear changes in urban heat island intensity, urban breeze intensity, and urban air pollutant concentration with roof albedo

This study systematically examines how the urban heat island (UHI) and urban breeze circulation (UBC) respond to an increase in roof albedo (αr) and its influence on urban air pollutant dispersion. For this, idealized ensemble simulations are performed using the Weather Research and Forecasting (WRF) model. The increase in αr from 0.20 to 0.65 decreases the UHI intensity, UBC intensity, and urban planetary boundary layer (PBL) height in the daytime (from 1200 to 1700 LST) by 47%, 36%, and 6%, respectively. As both UBC intensity and urban PBL height decrease, the daytime urban near-surface passive tracer concentration increases by 115%. The daytime UHI intensity, UBC intensity, and urban tracer concentration nonlinearly change with αr: For 0.10 ≤ αr < 0.80, the rates of changes in the UHI intensity, UBC intensity, and urban tracer concentration with αr overall increase as αr increases. For αr ≥ 0.80, the daytime roof surface temperature is notably lower than the daytime urban near-surface air temperature, the UHI intensity, UBC intensity, and urban tracer concentration very slightly changing with αr. This study provides insights into the associations between changes in roof surface temperature and roof surface energy fluxes with αr and those in UHI intensity.

Satellite-based estimation of monthly mean hourly 1-km urban air temperature using a diurnal temperature cycle model

Cities worldwide face escalating climate change risks, underscoring the need for spatially and temporally resolved urban air temperature (Ta) data. While satellite-derived land surface temperature (LST) data have been widely used to estimate Ta, high-resolution hourly Ta estimation in urban areas remains underexplored. Traditional methods typically rely on LST data from geostationary satellites and continuous 24-h Ta observations from weather stations. To address these limitations, we introduce a method that combines a diurnal temperature cycle (DTC) model with a random forest model to estimate monthly mean hourly urban Ta at 1-km resolution. This approach leverages a limited number of diurnal Ta observations from weather stations, MODIS LST data, and ancillary information. The core idea of the proposed method is to transform the estimation of monthly mean hourly 1-km Ta into estimating 1-km DTC model parameters, primarily daily maximum and minimum Ta values. This method capitalizes on MODIS LST's ability to estimate daily Ta extremes and requires only four diurnal Ta observations within a daily cycle to estimate monthly mean hourly 1-km Ta. Station-based five-fold cross-validation yields overall RMSE values consistently below 1.0 °C across nine cities with diverse geographic and climatic contexts. The accuracy achieved with only four diurnal Ta observations rivals that obtained using continuous 24-h Ta observations. Even with a limited training set of ten stations, the overall RMSE remains below 1.0 °C for most cities. The proposed method proves effective for both single-city and multi-city modeling and can estimate daily hourly 1-km Ta under clear-sky conditions. In conclusion, this study offers a feasible, efficient, and versatile method for accurately estimating monthly mean hourly 1-km Ta, which can be readily applied to other cities and holds potential for various applications.

Structural Uncertainty in the Sensitivity of Urban Temperatures to Anthropogenic Heat Flux

AbstractOne key source of uncertainty for weather and climate models is structural uncertainty arising from the fact that these models must simplify or approximate complex physical, chemical, and biological processes that occur in the real world. However, structural uncertainty is rarely examined in the context of simulated effects of anthropogenic heat flux in cities. Using the Weather Research and Forecasting (WRF) model coupled with a single‐layer urban canopy model, it is found that the sensitivity of urban canopy air temperature to anthropogenic heat flux can differ by an order of magnitude depending on how anthropogenic heat flux is released to the urban environment. Moreover, varying model structures through changing the treatment of roof‐air interaction and the parameterization of convective heat transfer between the canopy air and the atmosphere can affect the sensitivity of urban canopy air temperature by a factor of 4. Urban surface temperature and 2‐m air temperature are less sensitive to the methods of anthropogenic heat flux release and the examined model structural variants than urban canopy air temperature, but their sensitivities to anthropogenic heat flux can still vary by as much as a factor of 4 for surface temperature and 2 for 2‐m air temperature. Our study recommends using temperature sensitivity instead of temperature response to understand how various physical processes (and their representations in numerical models) modulate the simulated effects of anthropogenic heat flux.

On the Role of the Building Envelope on the Urban Heat Island Mitigation and Building Energy Performance in Mediterranean Cities: A Case Study in Southern Italy

The urban heat island (UHI) effect is one of the largest climate-related issues concerning our cities due to the localized temperature increase in highly urbanized areas. This paper aims to investigate the impact of UHI mitigation techniques in promoting climate resilience, by reducing urban air temperatures and cooling energy consumption in buildings. To this end, four mitigation solutions regarding the building envelope—green roofs, green walls, cool roofs, and cool walls—were investigated for the city of Bari in Southern Italy and compared with the current baseline scenario. Hence, five scenarios were simulated—using the ENVI-met microclimate software—during three representative summer days, and the resulting microclimate changes were assessed. Based on these analyses, new climate files—one for each scenario—were generated and used as input to run energy simulations in EnergyPlus to estimate the building cooling consumption. Coupling the microclimate and the consumption outcomes, the mitigation strategies were evaluated from both an urban and building point of view. The study shows that urban characteristics, mainly geometry and materials, are crucial for the UHI phenomenon. All the applied technologies seem to be effective. However, green walls proved to be more efficient in reducing outdoor temperatures (1 °C reduction in daily temperatures), while cool walls performed better in reducing cooling energy consumption, with an overall saving of 6% compared to the current scenario.

Examining the non-linear relationship between urban form and air temperature at street level: A case of Hong Kong

The relationship between urban form and localized air temperature has been studied extensively using linear regression. However, the findings remain inconsistent, and few studies have explored alternative data modeling techniques. With the rise of machine learning, there is an opportunity to explore new methods in urban climatology research. This study aims to test the hypothesis that machine learning models, rather than linear regression, can better explain and predict urban air temperature fluctuations in space and time. Measurements of air temperature were conducted at street level in Hong Kong. Urban form characteristics surrounding the measurement locations were extracted from geo-spatial databases and street view imagery. The datasets were then used to test the performance of linear regression, Artificial Neural Network (ANN), and Random Forest (RF) in predicting the spatial-temporal temperature fluctuations. The results indicate that the relationships between urban form and air temperature are predominantly non-linear. Both ANN and RF outperformed linear regression in prediction, with an MAE of 0.43 °C and 0.33 °C respectively. This study highlights the potentials of machine learning models in advancing knowledge of the impact of urban form on localized air temperature.

Impacts of Changes in Soil Moisture on Urban Heat Islands and Urban Breeze Circulations: Idealized Ensemble Simulations

Soil moisture plays important roles in land surface and hydrological processes, and its changes can greatly affect weather and climate. In this study, we examine how changes in soil moisture impact the urban heat island (UHI) and urban breeze circulation (UBC) through idealized ensemble simulations. As soil moisture increases, the latent heat flux increases considerably in the rural area. Hence, in the rural area, the sensible heat flux and surface temperature decrease, which decreases the rural air temperature. The decrease in rural air temperature leads to increases in UHI intensity and thus UBC intensity. The urban air temperature also decreases with increasing soil moisture since the cooler rural air is advected to the urban area by the enhanced low-level convergent flow of the UBC. However, the decrease in air temperature is smaller in the urban area than in the rural area. As the UBC intensity increases, the sensible heat flux in the urban area increases. The increase in sensible heat flux in the urban area further increases the UHI intensity. The positive feedback between the UHI intensity and the UBC intensity is revealed when soil moisture increases. The decrease in air temperature in both the urban and rural areas leads to the decrease in planetary boundary layer (PBL) height. As a result, the vertical size of the UBC decreases with increasing soil moisture. As the UBC intensity increases with increasing soil moisture, the advection of water vapor from the rural area to the urban area increases. Combined with the decrease in PBL height, this reduces the water vapor deficit or even leads to the water vapor excess in the urban area depending on soil moisture content.

Assessment of universal thermal climate index (UTCI) using the WRF-UCM model over a metropolitan city in India.

Rapid urbanization increases urban air temperature, considerably affecting health, comfort, and the quality of life in urban spaces. The accurate assessment of outdoor thermal comfort is crucial for urban health. In the present study, a high-resolution mesoscale model coupled with a layer Urban Canopy Model (WRF-UCM) is implemented over the city of Hyderabad (17.3850° N, 78.4867° E) to simulate urban meteorological conditions during the summer and winter period of 2009 and 2019. The universal thermal climate index (UTCI) has been estimated using the model-derived atmospheric variables and a human biometeorology parameter to assess the linkages between the outdoor environment and thermal comfort. Results revealed that during summer, the city experiences nearly 50h of very strong thermal stress, whereas about 120h of slight cold stress are experienced during winter. The urban area in Hyderabad expanded from 5 to 15% during the study period, leading to a 2.5℃ (2.8 ℃) increase in land surface temperature, and a 1.2 (1.9 ℃) rise in air temperature at 2m height and 1.5 (2.5 ℃) UTCI during summer (winter) time. The analysis reveals that the maximum UTCI values were noticed over built-up areas compared to other land classes during daytime and nighttime. The results derived from the present study have shown that the performance of WRF-UCM-derived UTCI reasonably portrayed the significant impact of urbanization on thermal comfort over the city and provided useful insights with regard to urban comfort and welfare.

Simulation advances with EnviBatE- A case study on urban heat island mitigation in Singapore

Urban morphology and surface thermal properties are two key factors that determine the local-level urban heat island effect. Among different strategies that help in mitigating this effect, applying high reflective cool coating on urban surfaces is one of the most effective ways for changes in both the daytime and night-time urban air temperatures. In this paper, advances in the EnviBatE microclimate model for district-level numerical simulations are presented and the results are compared against full-scale experimental measurements conducted in Singapore. Key aspects of the EnviBatE tool along with their mathematical models are demonstrated. The experimental site consists of two side-by-side street canyons located at an industrial estate in the west of Singapore: one canyon with conventional and the other with cool surfaces for side-by-side comparison. The results suggest that the drop in street-level canyon air temperature varies from 1 to 4 °C with the application of the cool surface coating on roofs, walls and roads in comparison with conventional canyon under various scenarios. By analysing the effects of the three types of built-up surfaces separately, the cool roads scenario appears to have the greatest impact on canyon air temperature (at 3 m above ground), followed by the cool roofs scenario. The cool walls scenario produces negligible cooling impact. The advantages of the cool coating application on urban surfaces in reducing air temperature are statistically analysed and demonstrated. The pedestrian comfort in terms of the Universal Thermal Climate Index (UTCI) is proposed to be integrated with EnviBatE and discussed using the simulation results.

Anthropogenic heat from buildings in Los Angeles County: A simulation framework and assessment

Anthropogenic heat (AH), i.e., waste heat from buildings to the ambient environment, increases urban air temperature and contributes to the urban heat island effect, which leads to more air-conditioning energy use and higher associated waste heat during summer, forming a positive feedback loop. This study used a bottom-up simulation approach to develop a dataset of the annual hourly AH profiles for 1.7 million buildings in Los Angeles (LA) County for the year 2018 aggregated at three spatial resolutions: 450 m, 12 km, and the census tract. Building AH exhibits strong seasonal and diurnal patterns, as well as large spatial variations across the urban areas. Building AH peaks in May and reaches a maximum of 878 W/m2 within one of several AH hotspots in the region. Among the three major AH components (surface convection, heat rejection from HVAC systems, and zonal air exchange), the surface convection component is the largest, accounting for 78% of the total building AH across LA County. Higher AH is attributed to large building density, a high percentage of industrial buildings, and older building stock. While AH peaks during the day, the resulting ambient temperature increases are much larger during the night. During the July 2018 heatwave in LA County, building AH (excluding the surface component) leads to a daily maximum ambient temperature increase of up to 0.6 °C and a daily minimum ambient temperature increase of up to 2.9 °C. It is recommended that reducing summer building AH should be considered by policy makers in developing mitigation measures for cities to transition to clean energy while improving heat resilience.

Chasing the heat: Unraveling urban hyperlocal air temperature mapping with mobile sensing and machine learning

Many cities face unprecedented high temperatures with increasing extreme events. Heatwaves pose significant health risks, including cardiovascular diseases, heatstroke, and dehydration. Mapping urban near-surface air temperature (Tair) is crucial for understanding thermal exposure and addressing climate change. Previous studies relied on satellite-derived land surface temperature (LST) and stationary monitoring, but high spaio-temporal Tair mapping is still a challenge. This study optimized a mobile sensing scheme using an electric bicycle platform with environmental and image sensors, and deep learning captured local-scale urban factors. A spatio-temporal data fusion model that consisted of three parts, temporal trend extraction, locality analysis, and neighborhood effect analysis, generated hyperlocal Tair maps. The Results from Beijing demonstrated the effectiveness of the framework, achieving the lowest MAE of 0.02 °C. Optimized data collection and the new model achieved accurate temperature predictions and thermal exposure assessment. Efficiency enhanced sensing strategy was also proposed. The study highlights local-scale factors and spatio-temporal dependencies in addressing heatwaves and climate change impacts in urban areas.

Implementing policies to mitigate urban heat islands: Analyzing urban development factors with an innovative machine learning approach

Taiwan is located in a hot and humid subtropical climate. Urban development, building heat emissions, and human activities lead to the urban heat island (UHI) effect in which urban air temperatures are significantly higher than those in the surrounding suburban areas. In this study, the factors contributing to the UHI effect were investigated in a case study of Taichung City, Taiwan by using high-resolution geographic climate data. Geographic Information System tools were used for grid-based spatial data analysis, and a physiological equivalent temperature index map was calculated and overlaid to identify heat zones. The results revealed that environmental factors have complex, nonlinear effects on temperatures in in urban areas; hence, the analysis was challenging. The decision tree algorithm was used to identified key environmental factors contributing to urban heat in areas with different levels of development; a weighting analysis was then performed to select effective urban cooling strategies. Finally, backpropagation neural network models tailored to different development levels were used for simulations to validate the proposed cooling policies. The results of this study were incorporated into the urban planning policies of Taichung City, Taiwan to mitigate the UHI effect and create a more suitable urban living environment.

Modeling urban air temperature using satellite-derived surface temperature, meteorological data, and local climate zone pattern—a case study in Szeged, Hungary

Urban air temperature is a crucial variable for many urban issues. However, the availability of urban air temperature is often limited due to the deficiency of meteorological stations, especially in urban areas with heterogeneous land cover. Many studies have developed different methods to estimate urban air temperature. However, meteorological variables and local climate zone (LCZ) have been less used in this topic. Our study developed a new method to estimate urban air temperature in canopy layer during clear sky days by integrating land surface temperature (LST) from MODIS, meteorological variables based on reanalysis data, and LCZ data in Szeged, Hungary. Random forest algorithms were used for developing the estimation model. We focused on four seasons and distinguished between daytime and nighttime situations. The cross-validation results showed that our method can effectively estimate urban air temperature, with average daytime and nighttime root mean square error (RMSE) of 0.5 ℃ (R2 = 0.99) and 0.9 ℃ (R2 = 0.95), respectively. The results based on a test dataset from 2018 to 2019 indicated that the optimal model selected by cross-validation had the best performance in summer, with time-synchronous RMSE of 2.1 ℃ (R2 = 0.6, daytime) and 2.2 ℃ (R2 = 0.86, nighttime) and seasonal mean RMSE of 1.5 ℃ (R2 = 0.34, daytime) and 1.2 ℃ (R2 = 0.74, nighttime). In addition, we found that LCZ was more important at night, while meteorological data contributed more to the model during the daytime, which revealed the temporal mechanisms of the effect of these two variables on air temperature estimation. Our study provides a novel and reliable method and tool to explore the urban thermal environment for urban researchers.

Variation the in relationship between urban tree canopy and air temperature reduction under a range of daily weather conditions

Mitigating heat is a vital ecosystem service of trees, particularly with climate change. Land surface temperature measures captured at a single time of day (in the morning) dominate the urban heat island literature. Less is known about how local tree canopy and impervious surface regulate air temperature throughout the day, and/or across many days with varied weather conditions, including cloud cover. We use bike-mounted air temperature sensors throughout the day in New Haven, Connecticut, USA, from 2019 to 2021 and generalized additive mixed models across 156 rides to estimate the daily variation in cooling benefits associated with tree canopy cover, and warming from impervious surface cover in 90 m buffers surrounding bike observations. Cooling is inferred by subtracting the bicycle-observed temperature from a reference station. The cooling benefits from tree canopy cover were strongest in the midday (11:00–14:00, −1.62 °C), afternoon (14:00–17:00, −1.19 °C), and morning (8:00–11:00, −1.15 °C) on clear days. The cooling effect was comparatively smaller on cloudy mornings −0.92 °C and afternoons −0.51 °C. Warming from impervious surfaces was most pronounced in the evening (17:00–20:00, 1.11 °C) irrespective of clouds, and during cloudy nights (20:00–23:00) and cloudy mornings 1.03 °C 95 % CI [1.03, 1.04]. Among the hottest observed days (top 25th percentile of reference station daily maxima), tree canopy was associated with lower temperatures on clear afternoons −1.78 °C [-1.78, −1.78], cloudy midday −1.17 °C [-1.19, −1.15], clear midday −1.12 °C [-1.12, −1.11]. We add a broader spectrum of weather conditions by explicitly including clouds, and greater temporal resolution by measuring throughout the day to bike-based urban heat research. Future mobile sampling campaigns may broaden the spatial extent with more environmental variation, representing an opportunity for public science and engagement.

Impacts of local climate zone mapping quality on urban near-surface air temperature simulation in WRF-UCM

Accurate simulation of urban near-surface air temperature (SAT) is essential for understanding climate dynamics. The reliability of the simulated SAT depends largely on the representation of urban morphology. Local Climate Zones (LCZ), as a sort of urban morphology representation, have been widely applied for SAT simulation based on the Weather Research and Forecasting Model coupled with urban canopy model (WRF-UCM). However, how the LCZ mapping quality affects the accuracy of SAT simulation remains underexplored. Here we investigate the relationship between the LCZ mapping quality and the accuracy of SAT simulation in WRF-UCM. The city of Guangzhou, China, is selected as the case study area. Six LCZ classifications are used to simulate SAT at the 1 km resolution, and the results are validated using the meteorological observations. The simulations suggest that the average values of root mean square error decrease if the more accurate LCZ classifications are used. The simulated SAT is also sensitive to the local mapping quality of LCZ classifications within 3 to 7 km windows. Our findings can provide insights for modelers to develop more accurate simulations of SAT, which are fundamental to the understanding of climate change impacts and promote relevant policy making toward sustainable cities.

Evaluation of the Urban Weather Generator on the City of Toulouse (France)

This article addresses the simulation of urban air temperatures with a focus on evaluating the Urban Weather Generator (UWG) model over Toulouse, France. As urban temperatures, influenced by factors like urbanization, anthropogenic heat release, and complex urban geometry, exhibit an urban heat island (UHI) effect, understanding and mitigating UHI become crucial. With increasing global warming and urban populations, aiding urban planners necessitates accurate simulations requiring data at the canyon level. The paper evaluates UWG’s performance in simulating air temperatures under realistic conditions, emphasizing an operational context and a non-specialist user’s perspective. The evaluation includes selecting the most suitable meteorological station, assessing the impact of the rural station choice, and conducting a sensitivity analysis of input parameters. The validation demonstrates good agreement, with a mean bias error (MBE) of 0.02 °C and a root mean square error (RMSE) of 1.73 °C. However, we highlight the fact that UWG performs better in a densely urbanized area, and exhibits limitations in sensitivity to urban surface parameter variations, particularly in less urbanized areas.

Analysis of Spatial Characteristics Contributing to Urban Cold Air Flow

To mitigate the urban heat island phenomenon at night, cool, fresh air can be introduced into the city to circulate and dissipate the heat absorbed during the day, thereby reducing high urban air temperatures. In other words, cold air flow (CAF) generated by mountainous and green areas should be introduced to as wide an area as possible within the city. To this end, it is necessary to first understand the characteristics of urban spatial factors that impact CAF, and to conduct concrete and quantitative analyses of how these urban spatial characteristics are contributing to air temperature reduction. In this study, the following are conducted: (1) an analysis of the relationship between cold air volume flux (CAVF) and the amount of air temperature reduction; (2) urban spatial categorization; (3) an analysis of the relationship between CAVF and the amount of air temperature reduction by urban spatial type; (4) a regression analysis between the amount of air temperature reduction and urban spatial characteristic factors that affect CAF; and finally, (5) the use of CAF to reduce urban air temperatures in urban planning and a design is proposed. Urban space was categorized into nine types using the results of the tertile analysis of CAVF and urban temperature reduction. It was determined that building height (BH) has a positive (+) influence on all urban spatial types, while building area ratio (BA) has a negative (−) effect. However, in the case of wall area index (WAI), the direction of influence varied depending on the development density; relatively low BA areas should focus on development that increases height to increase WAI, while relatively high BA areas should focus on development that reduces BA to reduce WAI by targeting development types closer to the tower type. And even in areas with similar development density, influence varies depending on the terrain elevation. Moreover, it is necessary to prepare improvement measures to increase the factors with CAF that positively influence air temperature reduction and decrease those with negative influence according to the characteristics of urban spatial types. Such results quantitatively and specifically confirmed the effects of spatial factors that affect CAF by urban spatial type on air temperature reduction. The results of this study can be used as useful information for the efficient use of CAF, a major element of urban ecosystem services.

Characterization of annual urban air temperature changes with special reference to the city of Modena: a comparison between regression models and a proposal for a new index to evaluate relationships between environmental variables

Characterization of annual urban air temperature changes with special reference to the city of Modena: a comparison between regression models and a proposal for a new index to evaluate relationships between environmental variables

Study on Microclimate and Thermal Comfort in Small Urban Green Spaces in Tokyo, Japan—A Case Study of Chuo Ward

Small urban green spaces are abundant in densely populated urban areas, but little is known about their impact on the urban heat island effect and thermal comfort. Therefore, this study selected as research sites four small urban green spaces in a typical high-density built-up area, Chuo Ward in Tokyo, Japan. The ENVI-met software 5.1.1 simulation method was used to analyze these sites’ microclimate and thermal comfort conditions. The following are the results: (1) Small urban green spaces significantly reduce urban air temperatures, particularly during hot weather, with temperature reductions ranging from 2.40 °C to 2.67 °C, consistently lower than the highest temperatures in Tokyo’s Chuo Ward, mainly between 1:00 and 2:00 p.m. (2) Thermal comfort analysis indicates that small urban green spaces can significantly improve urban thermal comfort during the day, particularly around noon, by reducing one or two thermal comfort levels compared to typical urban street blocks. However, these differences gradually diminish throughout the evening and night, and thermal comfort inside and outside green spaces becomes more uniform. (3) Green space size is not the only factor influencing thermal comfort; the layout of plants within the green space and the layout of the surrounding buildings also have an impact. Despite their small size, even small green spaces can significantly enhance comfort. This study highlights the need to promote urban sustainability through the extensive integration of small green spaces in dense urban environments. Small green spaces can serve as a high-frequency, low-cost solution for environmental sustainability by addressing the increasingly severe urban heat island effect as well as environmental challenges that in the urbanization process.

Impacts of urban air temperature and humidity on building cooling and heating energy demand in 15 cities of eastern China

Urban climates could significantly impact building energy consumption. This study aimed to investigate the impact of urban-rural disparities in temperature and humidity on building cooling and heating demand in multiple cities. Fixed stations were established using temperature/humidity loggers deployed at 17 pairs of urban and rural sites in 15 cities in eastern China. The study analyzed the differences in air temperature (ΔTa), relative humidity (ΔRH), and dew-point temperature (ΔTdew) between the urban and rural sites. These climatic data were then used as inputs for the energy simulation of a residential building. The following results were revealed: (1) ΔTa was consistently higher at night than during the day. In comparison with the rural reference sites, the RH values at all urban sites were lower; however, some urban sites displayed lower Tdew, while others exhibited higher Tdew. (2) Regarding annual total energy demands, urban buildings exhibited variable changes in energy components compared to rural buildings. Sensible cooling demands increased by 10 %–36 %, while heating demands decreased by 9 %–30 %. Latent cooling demands ranged from a decrease of 12 % to an increase of 17 %. As a result, overall changes in annual total energy demands (cooling + heating) were relatively small, ranging from −2 % to +7 %. (3) Regarding daily total energy demands, a 1 °C increase in ΔTa led to a 12.1 % increase in sensible cooling demand and an 8 % decrease in heating demand. At the same time, a 1 °C variation in ΔTdew corresponded to a 14.6 % change in latent cooling demand. (4) Regarding the daily peak load, modifications in local temperature and humidity in urban areas had a more pronounced impact on daily peak heating loads than on daily peak cooling loads. This study could enhance our understanding of how urban local climates affect building cooling and heating energy demands.