Urban Climate最新文献

{"title":"Spatiotemporal distribution characteristics of thermal environment in high-density urban areas under the influence of building air conditioning heat rejection","authors":"Kang Mu,&nbsp;Pengcheng Wang,&nbsp;Sihui Li,&nbsp;Changwei Jiang,&nbsp;Qiong Suo,&nbsp;Xiaoqin Sun","doi":"10.1016/j.uclim.2026.102973","DOIUrl":"10.1016/j.uclim.2026.102973","url":null,"abstract":"<div><div>Urban air conditioning (AC) heat rejection significantly influences the thermal and humidity environment of high-density cities, yet the coupled impacts of sensible and latent heat release in mixed-use districts remain insufficiently quantified. This study investigated the spatiotemporal thermal-humidity effects of AC systems in a high-density mixed-use urban area in Changsha, China, through field measurements and computational fluid dynamics (CFD) simulations. Results showed that residential split-type AC outdoor units continuously released sensible heat, increasing air temperature by approximately 5 °C during daytime and up to nearly 8 °C at night. In dense residential areas, the maximum nighttime temperature increase reached 4.15 °C at approximately 20 m height. In contrast, commercial cooling towers mainly discharged latent heat, with exhaust relative humidity remaining near saturation (95–100%). During peak daytime operation, evaporative cooling reduced air temperature around commercial buildings by approximately 1.3 °C, while increasing surrounding relative humidity by 4–6%. CFD simulations indicated that AC heat rejection modified airflow structures, increasing maximum wind speed from below 2.5 m/s to 4.96–6.13 m/s during daytime and 5.13–7.10 m/s at night. The downstream diffusion distance of heat and moisture reached approximately 800–900 m during daytime and decreased to around 500 m at night. The validated CFD model showed measured thermal and humidity distributions with temperature errors below 4.94% and humidity errors below 2.06%. The results quantitatively demonstrate the distinct thermal and humidity impacts of sensible and latent heat rejection and provide guidance for mitigating localized thermal stress in high-density urban districts.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102973"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The non-linear and indirect impacts of urban features on urban Heat Island intensity","authors":"Yuxi Ren ,&nbsp;Jing Wen ,&nbsp;Jianguo Feng ,&nbsp;Xiaona Wang ,&nbsp;Haitao Wang ,&nbsp;Bing Xie ,&nbsp;Chen Zhang ,&nbsp;Qi Wang","doi":"10.1016/j.uclim.2026.102986","DOIUrl":"10.1016/j.uclim.2026.102986","url":null,"abstract":"<div><div>Urban Heat Island (UHI) effect poses a direct threat to human health and sustainable development. The correlation between surface urban heat island intensity (SUHII) and urban feature factors has been extensively studied, but the non-linear correlation patterns and indirect pathways, remain insufficiently understood. To address this gap, we conducted an investigation of 952 cities globally using an explainable AI and structural equation modeling (SEM) frame work. Our study found that: (1) The importance of urban feature factors on SUHII varied between day and night. During the daytime, SUHII is primarily regulated by leaf area index difference (ΔLAI), precipitation (Pre), enhanced vegetation index difference (ΔEVI), percentage of tree (PT), elevation (Ele), shape index (SI), whereas at night it is mainly affected by albedo difference (ΔAlb), PT, ΔEVI, Pre, evapotranspiration (ET), landscape shape index (LSI). (2) Non-linear relations have been detected. During the daytime, Pre and PT showed a positive correlation with SUHII; at night, ΔEVI and ET exhibited a negative correlation with SUHII. (3) Less important factors in machine learning (ML) could affect as SUHII through important factors. During the daytime, building height (BH) contributes to higher Pre, thereby exacerbating the UHII; at night, however, BH suppresses ΔEVI, which alleviates it. A relationship uncovered only through our SEM validation. This study quantifies the global diurnal and general driving factors in SUHII and reveals often-overlooked indirect pathway, providing scientific evidence for sustainable resilient urban planning.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102986"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attributing urban heat islands with a surface energy balance: Biophysical drivers across climate gradients","authors":"Tianhan Li ,&nbsp;Deyong Hu ,&nbsp;Yu Zhou ,&nbsp;Roger Cremades","doi":"10.1016/j.uclim.2026.102968","DOIUrl":"10.1016/j.uclim.2026.102968","url":null,"abstract":"<div><div>Research on the urban heat island (UHI) and its driving mechanisms has become a major topic in urban climate studies. UHI is ultimately controlled by urban–rural differences in surface energy exchange. In this paper, an Attribution model of Surface Urban Heat Island (ASUHI) was developed for daytime clear-sky surface UHI attribution within the framework of surface energy balance. Surface UHI intensity was decomposed into five temperature contribution terms: net radiation term (<em>∆T</em>_<em>Rn</em>), convection term (<em>∆T</em>_<em>Con</em>), evapotranspiration term (<em>∆T</em>_<em>Eva</em>), heat storage term (<em>∆T</em>_<em>HS</em>), and anthropogenic heat term (<em>∆T</em>_<em>AH</em>). The model was applied to four typical Chinese cities (Beijing, Changsha, Guangzhou, and Yinchuan) using satellite remote sensing products and atmospheric reanalysis data. It reproduced surface UHI patterns with correlation coefficients of about 0.53–0.83, RMSE of 1.31 to 2.72 K, and MAE of 0.94 to 2.33 K. <em>∆T</em>_<em>AH</em> was consistently positive (0.16 to 0.65 K), whereas <em>∆T</em>_<em>Con</em> and <em>∆T</em>_<em>HS</em> acted as cooling terms (−0.32 to −1.88 K and − 0.57 to −2.68 K, respectively). <em>∆T</em>_<em>Rn</em> remained small (−0.61 to 0.27 K). The largest inter-city contrast occurred in <em>∆T</em>_<em>Eva</em>: in summer it reached 2.88 K in Changsha, 2.30 K in Guangzhou, and 1.82 K in Beijing, but only 0.49 K in arid Yinchuan. Based on the four cities examined, daytime energy redistribution in winter was dominated by convection, whereas summer redistribution shifted toward evapotranspiration in the more humid cities. This study improves the interpretability of daytime surface UHI attribution across contrasting climatic backgrounds.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102968"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148168005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding the urban heatscape: Suburb-level analysis of long-term extreme heat hazard in Brisbane","authors":"Sk Tahsin Hossain ,&nbsp;Tan Yigitcanlar ,&nbsp;Yan Liu ,&nbsp;Zhong Ren Peng","doi":"10.1016/j.uclim.2026.102983","DOIUrl":"10.1016/j.uclim.2026.102983","url":null,"abstract":"<div><div>Extreme heat poses a growing threat to Australian cities, yet traditional assessments often overlook its local variability. This study analyses suburb-level heat hazard associated with extreme heat events in Brisbane using over five decades (1971–2024) of daily temperature data from 47 weather stations. Instead of satellite proxies or demographic models, it applies Australia's Bureau of Meteorology Extreme Temperature Indices (ETIs) to capture actual daytime and nighttime thermal conditions. Seven ETIs were assessed using a triangulated approach combining Mann-Kendall trend analysis, <em>Z</em>-score anomaly detection, and Getis-Ord Gi* spatial clustering. Results were synthesised into a Heat Hazard Index (HHI) and spatially interpolated across 190 suburbs. Findings reveal a distinct east–west and day–night divide: nighttime heat is more spatially widespread, especially in dense eastern and central areas, while daytime extremes are concentrated inland and west. Overall, 188 suburbs exhibit elevated heat hazard across at least one form of ETI, with 109 experiencing high heat hazard across both daytime and nighttime conditions. These findings highlight the limitations of city-wide averages and emphasise the importance of spatially targeted climate adaptation strategies. The framework, developed for the Australian context with long-term observational datasets such as the Scientific Information for Land Owners (SILO) climate database, offers a scalable approach for assessing urban heat hazard in cities with comparable monitoring networks. By translating empirical climate observations into suburb-scale insights, the study contributes to data-driven and equitable urban climate planning aligned with global sustainability frameworks including the UN Sustainable Development Goals and the New Urban Agenda.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102983"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148231316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Roles of the intrinsic characteristics and the surrounding landscapes in the inside and outside cooling of urban tree canopy patches","authors":"Kuanbiao Qiu ,&nbsp;Baoquan Jia","doi":"10.1016/j.uclim.2026.102912","DOIUrl":"10.1016/j.uclim.2026.102912","url":null,"abstract":"<div><div>Urban greening is essential for alleviating urban heat island effect. Due to the joint influence of turbulent heat exchange and surface roughness, a temperature interaction area (TIZ) inside and outside the patch is formed near the boundary of the green space patch. This area spans patch boundaries and experiences dramatic temperature changes. The area outside the patches is termed as the outside cooling, while the area inside the patch is termed as the inside cooling. Extensive studies have focused on the outside cooling. However, few studies have focused to the inside cooling extent and compared these two types of cooling effects. Therefore, this study automatically extracted the TIZ near the UTC patch boundary based on the remote sensing method and calculated the inside and outside cooling provided by the UTC patch. Subsequently, the ridge regression analysis was conducted to explore the mechanisms by which factors affect the inside and outside cooling. Results showed that the distances of the inside and outside cooling were approximately 73.18 m and 98.50 m, respectively, while the cooling amplitudes were 1.12 K and 1.37 K, respectively. The shape of the medium-to-large tree-covered patches exerted great impact on the distance and amplitude of the inside cooling, while the surrounding landscape pattern played a vital role in the outside cooling. This indicated that different strategies should be made according the use of the space under the tree canopy and its adjacent area by the residents when establishing the local-adapted measurements to mitigating the urban heat island effect.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102912"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148231452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel instrument for high-resolution urban microclimate mobile-measurements","authors":"Ignacio Requena-Ruiz,&nbsp;Daniel Siret","doi":"10.1016/j.uclim.2026.102926","DOIUrl":"10.1016/j.uclim.2026.102926","url":null,"abstract":"<div><div>Microclimate mobile measurements have emerged as valuable tools for capturing high-resolution climatic data at human scale in urban areas. However, their broader adoption remains constrained by persistent methodological and technical challenges. This article presents a comprehensive review of 25 recent case studies and identifies five recurring challenges. In response, we propose and validate a new instrument design equipped with fast response sensors, GNSS-RTK for sub-meter geolocation and custom applications for traverse design, navigation and data visualization. The experimental validation analyses sensor behaviour in mobile conditions and measurements spatial accuracy. Results show minimal measurement variability for air temperature, relative humidity and both short- and long-wave radiation (&lt;2.3%), while wind speed showed higher variability due to its intrinsic instability in urban contexts. Radiometers achieved 90% stabilization within 1.5 s, confirming their suitability for detecting sub-meter radiative events at speeds of around 0.6 m·s⁻¹. GNSS-RTK improved mean geolocation precision to 0.34 m under optimal conditions. These findings demonstrate that well-calibrated mobile instruments, equipped with appropriate sensors and protocols, can produce inframetric-resolution data for analysing urban microclimate variability at the human scale. This is essential for informing design decisions and evaluating human thermal conditions in urban environments.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102926"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Downscaling urban thermal signals with machine learning: A case study of Danish functional urban areas","authors":"Maria Castro ,&nbsp;João Paixão ,&nbsp;Inês Girão ,&nbsp;Bruno Marques ,&nbsp;Rune Magnus Koktvedgaard Zeitzen ,&nbsp;Rita Cunha ,&nbsp;Caio Fonteles ,&nbsp;Peter Thejll ,&nbsp;Hjalte Jomo Danielsen Sørup ,&nbsp;Quentin Paletta ,&nbsp;Ana Patrícia Oliveira","doi":"10.1016/j.uclim.2026.102947","DOIUrl":"10.1016/j.uclim.2026.102947","url":null,"abstract":"<div><div>As climate change prospects point towards the pressing need for local-scale adaptation measures, it becomes of utmost importance to recognise intra-urban hotspots of excessive heat exposure. Accordingly, there is a pressing societal need to downscale the available near-surface air temperature predictions (reanalysis, forecasts, or projections) from the regional (kilometric) to a local (sub-kilometric) resolution, to facilitate the identification of short-term critical areas within metropolitan regions of cities, where acclimatisation and public health preventive measures actions should be prioritised. This work presents a Machine Learning (ML)-based methodology for upsampling near-surface air temperature from a 2.5 × 2.5Km to a 125 × 223 m (0.002 × 0.002°) grid, the Urban Thermal Signal Downscaling (UTS-D) model. The model was developed over four Danish Functional Urban Areas – Copenhagen, Aarhus, Aalborg and Odense - using crowdsourced data from citizen-own weather stations, along with geospatial predictors that are well-known determinants of the Urban Heat Island (UHI) effect. The best overall results were achieved with a Random Forest model with an overall Mean Absolute Error (MAE) of 0.76 K for the overall test subset, 0.66 K during heatwave and 0.85 K during coldwave subsets, denoting the model's good performance under extreme heat conditions. When evaluating against reference in-situ stations, the model's accuracy shows a slight decrease compared to the background numerical weather predictor model, suggesting that, by training solely on crowdsourced stations (clustered in urban areas), there is a tendency towards overestimating rural air temperatures. Nevertheless, regarding the spatial pattern, the model captures the thermal signature of the underlying urban infrastructure, allowing to disclose neighbourhood-level details.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102947"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148168003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Microclimate monitoring and modelling in a regenerating urban environment: Milano (Italy)","authors":"Luca Gallia ,&nbsp;Stefano Basiricò ,&nbsp;Shamila Haddad ,&nbsp;Thomas Parkinson ,&nbsp;Aysu Kuru ,&nbsp;Akisha Nomoto ,&nbsp;Roberto Colombo ,&nbsp;Riccardo Castellanza ,&nbsp;Federico Agliardi","doi":"10.1016/j.uclim.2026.102961","DOIUrl":"10.1016/j.uclim.2026.102961","url":null,"abstract":"<div><div>Urban regeneration projects in densely populated areas often involve limited, small-scale interventions. The present study provides a data-driven evaluation of solutions to mitigate the urban heat island effect at microscale, integrating densely distributed multisensory and multitemporal monitoring, material characterization, and microscale modelling. A dedicated monitoring system was utilized to measure microclimatic variables during the various phases of regeneration of a compact public square in Milan (Italy). The observations were utilized to establish boundary conditions and to calibrate and validate a microclimatic numerical model for a typical summer day. Three configurations representing different stages of regeneration were analyzed: pre-regeneration conditions, post-regeneration, and a future planned condition. The results obtained from the study indicate that changes in near ground air temperature were limited, whereas surface temperatures and radiative conditions exhibited clearer contrasts linked to the optical and thermal properties of materials and to shading. Reflective paving reduced surface heating in some locations but did not improve mean radiant temperature under peak solar exposure, with local increases that may penalize pedestrian comfort. The findings of this study suggest that the most significant reductions in radiative heat load and the most marked improvements in thermal comfort are produced by mature tree shading. This highlights the importance of optimizing shade placement relative to pedestrian-use areas and of considering soil moisture and maintenance. The objective is to provide support in optimizing mitigation strategies in regeneration projects, highlighting positive aspects and critical points in the distribution of solutions designed to benefit both the microclimate and pedestrian comfort.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102961"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148231442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How effectively can climatic and morphological parameters predict urban microclimate in hot semi-arid residential neighbourhoods? A validated regression-based approach","authors":"Archana Singh ,&nbsp;Tarush Chandra ,&nbsp;Sanjay Mathur ,&nbsp;Jyotirmay Mathur","doi":"10.1016/j.uclim.2026.102979","DOIUrl":"10.1016/j.uclim.2026.102979","url":null,"abstract":"<div><div>Despite global efforts toward sustainable urban development, predictive methods for assessing outdoor thermal comfort (OTC) remain limited and computationally intensive, particularly in hot semi-arid cities such as Jaipur where increasing heat stress and inadequate thermally informed planning hinder effective climate-responsive urban design.</div><div>This study develops a validated regression-based framework integrating eleven morphological parameters with reference weather variables to predict neighbourhoodscale microclimate across Local Climate Zones (LCZs) in Jaipur for both summer and winter seasons. Micrometeorological data were collected at 66 locations using a Testo 480 device, with Multiple Linear Regression (MLR) applied to analyze the combined influence of climatic and morphological parameters on air temperature, relative humidity, wind speed, and solar radiation.</div><div>The summer Ta model demonstrated strong predictive performance (R² = 0.82) and the winter model moderate fit (R² = 0.66). Bootstrap predictor-selection analysis (B = 1000 resamples) confirmed stable selection of dominant predictors, reference air temperature, building height, and sky view factor regardless of sample composition. Leave-one-LCZ-out cross-validation (k = 11) yielded a mean predictive R² of 0.692 and RMSE of 1.33 °C for summer, comparable to the 80–20 split results, confirming spatial generalisability beyond the sampled locations.</div><div>Among morphological parameters, vertical area ratio outperformed floor area ratio, and impervious surface fraction exceeding 55% was associated with elevated summer air temperatures. Height variability and courtyard orientation emerged as additional thermal determinants, highlighting their potential as actionable parameters for climateresponsive design in greenfield and brownfield projects, contributing to thermal resilience in hot semi-arid cities.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102979"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148167888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A mesoscale CFD framework for extreme temperature mapping in urban environments","authors":"Alessia Piazza ,&nbsp;Girma T. Bitsuamlak ,&nbsp;Maria Pia Repetto ,&nbsp;Josip Žužul ,&nbsp;Massimiliano Burlando","doi":"10.1016/j.uclim.2026.102978","DOIUrl":"10.1016/j.uclim.2026.102978","url":null,"abstract":"<div><div>This study presents a combined statistical and numerical framework for mapping extreme temperatures in a complex urban environment. The municipality of Genoa, a coastal city in Italy, was used as a case-study example. Historical temperature data from 14 meteorological stations were analyzed using the Generalized Extreme Value (GEV) distribution to establish a robust statistical characterization of extreme temperatures across the region. A detailed three-dimensional model of the city, incorporating topographic and land-cover information, was developed for conducting Computational Fluid Dynamics (CFD) simulations using OpenFOAM. Steady-state Reynolds-Averaged Navier-Stokes (RANS) simulations were performed to reproduce the thermal conditions associated with extreme summer and winter events. The simulated temperature fields were validated against observations from the weather stations, showing good overall agreement. The resulting datasets were used to generate high-resolution maps of extreme maximum and minimum temperatures, highlighting the spatial variability temperatures within the city, that is seen to be locally affected by terrain features like topography and texture. The proposed methodology demonstrates the potential of integrating statistical extreme value analysis with CFD modeling to support evidence-based urban planning and the development of targeted extreme temperature mitigation and adaptation strategies, also in the context of climate change, in complex urban environments.</div></div>","PeriodicalId":48626,"journal":{"name":"Urban Climate","volume":"67 ","pages":"Article 102978"},"PeriodicalIF":6.9,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148230609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}