Madrid is located in a topographically complex environment influenced by the Guadarrama mountain range, where thermally-driven flows (TDFs), such as mountain–valley breezes, interact with the urban heat island (UHI) and modulate local meteorological conditions. Despite the well-documented warming associated with increasing urbanisation, the coupling between TDFs and the UHI remains insufficiently characterised.Long-term observational datasets (1961–2023) from urban and rural meteorological stations reveal significant warming trends, more pronounced in summer than in winter, together with marked differences between maximum and minimum temperatures. The UHI intensity, defined from minimum temperature differences, shows mean values close to 2 °C (depending on the rural reference station), and exhibits a strong dependence on synoptic conditions, reaching up to ~3.3 °C under stable situations, which are, indeed, ideal conditions for TDF development.The variability of TDFs is analyzed using both long-term observations and specific field campaigns, focusing on the diurnal and seasonal cycles as well as their frequency, intensity and directional patterns. These circulations play a key role in urban ventilation, air quality, and boundary layer structure, with significant implications for thermal comfort and population vulnerability.The interaction between TDFs and the UHI is further examined through mesoscale simulations using the Weather Research and Forecasting (WRF) model with the BEP-BEM urban canopy scheme (Carbone et al., 2024; Salamanca et al., 2010; Martilli et al., 2002). The results highlight a two-way coupling: urban-induced thermal anomalies modify local circulations, while TDFs influence the intensity and spatial distribution of the UHI.These findings provide new insights and an integrated view of the interactions between urbanization, topography, and atmospheric dynamics in complex environments, contributing to the improvement of urban representation in numerical models and supporting the development of climate adaptation strategies.

Urban weather and climate modeling is challenged by the highly heterogeneous and dynamic nature of cities. It exhibits a persistent trilemma between spatial granularity, spatiotemporal coverage, and physical interpretability. We articulate this challenge and propose a hybrid framework integrating physics-based models, urban observations, and machine learning. Framing this challenge as an integration problem across methods and scales, we provide a structured guide for next-generation, decision-relevant urban weather and climate modeling.

Climate change is a well-known topic for most people nowadays, being present in a considerable proportion of conversations worldwide. Albeit scientists have a data-based perception of the global climate change, most people relationship with it is based on their daily experiences and memories, which can be unreliable especially in the medium and long ranges. One statement commonly heard by the authors in relation to people’s experience with climate change is that intermediate seasons are retreating, leading to a cold-hot season dipole. This work aims to verify if data confirm this general perception using quality-controlled observational data. In this study, we select over 1000 climatological stations around the world and analyse the trends in the number of days belonging to each climatological season. For a station to be selected, a simple two-step procedure is followed to ensure the reliability of the trends computation: 1) any year with at least 20 days without data is removed from the time series; 2) then, if a station has less than 8 years with remaining data in any decade, that station is discarded from the selection. Once station selection is performed, the number of days belonging to one season is computed by creating a yearly cycle of temperature using data between 1955 and 1984 as climatological reference period. Then, the days belonging to a climatological season are computed as follows for the northern (southern) hemisphere stations: a) days with a temperature above (below) the climatological mean of June 1st are considered summer (winter) days; b) days with a temperature below (above) the value of the climatological mean of December 1st are considered winter (summer) days; c) values in between have been considered as spring or autumn days, depending if they occur before the yearly higher peak of temperature, or after it. Both daily maximum and minimum temperatures, and several reference periods, are used for the analysis with similar results in all cases. We find that, in general, the durations of intermediate seasons have remained steady or slightly increased since 1955 around the world, in contrast with the popular feeling. Furthermore, the length of summer has increased in Europe, Australia and China at a rate of 3-4 days/decade in average, whereas the number of winter days has decreased in those regions at a rate of between 5 and 10 days/decade. However, these trends are not as clear in the continental United States of America, with a region in central USA with a reverse trend –longer winters and shorter summers. These findings highlight the importance of data availability for climatological studies and discussions and reveal an area of interest inconsistent with the general global trend, which should be further studied.

Urban heat adaptation strategies are critical for mitigating the impacts of ex- treme heat events in cities, particularly as climate change exacerbates their intensity and frequency. This study evaluates a set of adaptation strategies during the 2023 heatwave in Grenoble, France, using the WRF model with the BEP + BEM urban canopy scheme. Eight scenarios are simulated, including increased vegetation, reflective surfaces, enhanced building insulation, permeable surfaces, and combined strategies, assessing their effects on air temperature, thermal comfort (UTCI), and energy demand. The results highlight that strategies involving vegetation, reflective surfaces, and building insulation are particularly effective in reducing temperatures and improving thermal comfort, with tree-based inter- ventions showing the greatest impact, primarily through shading, while evapotranspiration is only represented through a simplified empirical parameterization. Combined strategies enhance these effects, providing near-additive reductions in heat stress, especially during the day, with median daytime UTCI reductions of up to approximately 1.9 °C and a reduction in the most severe exposure class (38–46 °C) from 4.8% to 0.1%. Building-focused inter- ventions (e.g. insulation) significantly lower energy demand (up to 62% reduction compared to the control), whereas ground-based strategies (e.g. permeable ground) primarily improve outdoor thermal comfort. Urban morphology strongly influences the effectiveness of these measures, with densely built areas (e.g. compact LCZs) benefiting the most in terms of ab- solute reductions. This work emphasizes the importance of integrating diverse strategies to address urban heat, bridging immediate mitigation efforts with long-term resilience planning.

Accurately capturing the spatial variability of urban heat exposure is important for planning heat-resilient cities. While regional climate models have historically simplified urban characteristics, high-resolution urban morphological datasets now present an opportunity to produce spatially accurate heat maps. In this vein, this study evaluates four morphological datasets for Sydney, Australia in the Weather Research and Forecasting (WRF) model during the extremely hot period of 10-20 January 2017: the default IGBP-MODIS data with no local morphology, a class-based Local Climate Zones (LCZ) dataset requiring parameter interpretation by modellers, and explicitly defined parameter datasets from World Settlement Footprint 3D (WSF-MB) and Geoscape. The latter three used the BEP-BEM-Comfort urban canopy model, which outputs subgrid-scale Universal Thermal Climate Index (UTCI), an indicator of human thermal stress. Comparison with observations showed any urban dataset over WRF default reduced 2m temperature mean absolute errors (MAEs) at peak solar hours by at least 1°C on average, while localized instantaneous and median temperature differences reached 13°C and 4.5°C across the domain. High-resolution gridded experiments outperformed LCZ temperature predictions by up to 0.37°C (MAE) before sunrise. LCZ and Gridded experiments revealed substantial UTCI differences, with LCZ predicting four times lower probability of extreme heat stress in the afternoon, and 2-3°C lower average UTCI exceedances in Western Sydney. Urban dataset choice mattered most under weak synoptic conditions, though simplified datasets could have critical discrepancies in estimating localised heat stress even during strong forcing. The findings underscore the importance of high-resolution data for identifying heat-vulnerable times and locations.

Madrid is located in a topographically complex environment, with the Sierra de Guadarrama being the most relevant mountain system in the area, where thermally-driven flows (TDFs), such as mountain and valley breezes, interact with the urban heat island (UHI) and modulate local meteorological conditions. Over recent decades (1970–2020), the population of Madrid has doubled while the urbanized area has expanded by a factor of five. Future projections indicate a further urban expansion of 1.15 to 2.14 times the 2010 extent, accompanied by an approximate 15% population increase by 2037 (Gao & Pesaresi, 2021; INE, 2022). In this context, understanding how urbanization modifies wind regimes through changes in surface properties and terrain roughness, as well as its interaction with the UHI, is essential.The main objective of this study is to characterize the TDFs affecting Madrid and to analyze their interaction with the UHI, assessing their spatial and temporal variability and their influence on the thermal and dynamical structure of the urban atmospheric boundary layer. The study is based on long-term observational and statistical analysis of meteorological datasets from urban and rural stations, complemented by field campaigns. These observations allow for the assessment of diurnal, seasonal, and annual variations in wind patterns, with a particular focus on detecting and characterizing breeze events, as well as quantifying differences in their intensity, direction, frequency, and duration between the urban environment and the surrounding mountainous areas.In addition, numerical simulations are performed using the mesoscale Weather Research and Forecasting (WRF) model with advanced urban schemes, such as BEP-BEM (Martilli et al., 2002; Salamanca et al., 2010; Carbone et al., 2024), to further explore the underlying physical processes and to assess the impact of urbanization and thermally-driven flows on thermal comfort and air quality.This research is part of the MULTIURBAN-II and AIRTEC2-CM projects. The results are expected to advance the understanding of urban atmospheric processes in topographically complex settings and provide critical information for urban planning and climate adaptation strategies.

Sea-land breezes play multiple roles in coastal environments. Among them, they moderate urban temperatures and enhance ventilation, preventing extreme heat and pollution accumulation during extended periods of atmospheric stability. However, they can also transport ozone precursors inland, shifting air quality impacts to rural regions. Despite their importance, high-resolution projections of sea-land breeze and ozone interactions under future climates remain scarce. To address this critical gap, this study provides a high-resolution (1 km) projection of the sea-land breezes response in the Metropolitan Area of Barcelona under the SSP3-7.0 scenario for 2050 and 2100. Here we apply the Pseudo Global Warming approach with the WRF-Chem model and BEP + BEM urban canopy scheme. Our simulations reveal a novel climate-driven shift: an acceleration of the breeze parallel to the coastline, reducing inland penetration and delaying the breeze front by 1-2 h by 2100. These changes, combined with rising temperatures, modify planetary boundary layer dynamics, which substantially alter ozone formation and transport. Consequently, we project significant ozone increases by 2100, trapping this pollutant particularly in densely populated coastal zones, raising new concerns about future health risks and the need for adapted mitigation strategies.

The need for cities to prepare for the increasing frequency, persistence, and intensity of heat waves (HWs) makes modeling these events essential for evaluating the effectiveness of heat adaptation strategies. We use the Pseudo Global Warming (PGW) method to project HW episodes that are simulated with the Weather Research and Forecasting (WRF) model coupled with the Building Effect Parameterization and Building Energy Model at 1 km resolution using the Metropolitan Area of Barcelona (AMB) as a case study. We assess three plausible urban adaptation strategies to reduce temperatures, heat stress and vulnerability to heat now and in mid- and late21st century conditions under the SSP370 radiative forcing scenario: 1) increasing rooftop albedo by white-painting all feasible rooftops; 2) implementing irrigated sedum green roofs where possible; and 3) increasing peri-urban agriculture, urban parks, and urban fraction while reducing urban forest according to the recently approved Urban Master Plan. We find that (1) provides the greatest cooling (-1.75 degrees C during daytime) in the most vulnerable areas, while (2) and (3) show moderate regulation (-0.37 degrees C and - 0.26 degrees C respectively) and slight nighttime warming (0.24 degrees C and 0.31 degrees C). Despite these reductions, none fully counterbalance the projected 6 degrees C increase by 2100, highlighting the limited capacity of adaptation strategies under severe warming scenarios and providing critical insights into effective and feasible measures to mitigate heat impacts and reduce vulnerability in urban environments.

Street-level heat exposure, humidity, and cooling energy demand are governed by turbulent exchange within the urban canopy, yet operational models often oversimplify turbulence and miss vertical variability. Using high-resolution large-eddy simulations (LES) and published syntheses, we represent the non-monotonic structure of canopy turbulence via a unified multi-layer closure valid across urban canopy densities and test its city-scale implications in Chicago. Embedded in a 1-D RANS framework, the proposed closure reduces mixing-length and eddy-viscosity errors by 70% and 50% relative to LES. Simulations better match observed temperature and humidity and reveal 1.5–2.5 °C higher thermal exposure and ±$3–6% RH differences in moderately dense local climate zones, consistent with increased cooling-energy demand. Results are robust across coarse and high-resolution morphology, showing that turbulence—not merely morphology detail—controls neighborhood-scale outcomes. By resolving canopy mixing that current schemes oversimplify, this work reduces decision-relevant uncertainty and enables reliable street-level assessments for urban design, operations, and resilience planning.

During the summer of 2025 (23 June–13 July), an intensive meteorological and turbulence observation campaign was conducted in central Madrid within the framework of the AIRTEC2-CM and MULTIURBAN-II projects. Measurements combined data from a permanent meteorological station and a portable high-frequency eddy-covariance system (IRGASON) from the GuMNet network, both installed on a rooftop at 27 m above ground level. Standard meteorological variables, radiative fluxes, and key turbulence parameters, including friction velocity, turbulent kinetic energy, and sensible heat flux, were recorded.The observational period was dominated by persistent anticyclonic conditions over the Iberian Peninsula, leading to strong atmospheric stability, weak synoptic forcing, and positive geopotential height anomalies at 500 hPa. These conditions favoured the development of thermally driven mesoscale circulations, particularly nocturnal breezes, which interacted with the urban boundary layer and modulated turbulence and mixing processes. Diurnal cycles of meteorological and turbulent variables are analysed with particular emphasis on the evening transition and the nocturnal stable boundary layer.Several episodes characterized by very stable conditions and elevated NO₂ concentrations (exceeding 100 μg m⁻³) were observed. The onset of nocturnal breezes was associated with enhanced turbulent mixing and a rapid decrease in pollutant concentrations. High-resolution simulations with the WRF mesoscale model are also presented to evaluate its ability to reproduce the observed thermally driven circulations and their impact on the nocturnal urban boundary layer. Overall, the results highlight the key role of mesoscale thermally driven flows in regulating turbulence, mixing, and scalar transport in urban environments under weak synoptic forcing.

Urban areas in the Mediterranean basin are increasingly exposed to thermal stress as a result of climate change and ongoing urbanization, creating an urgent need for urban climate information that supports heat-risk assessment and adaptation strategies at city scale. This study presents an integrated multiscale assessment of the urban microclimate in Bari (southern Italy), a mid-sized Mediterranean coastal city, with the aim of disentangling the relative contributions of sea–land breeze dynamics and urban morphological characteristics to intra-urban thermal variability. The analysis combines three complementary approaches. First, in situ observations of air temperature and relative humidity were collected during summer 2023 using a dense network of eight canyon-level sensors distributed across neighborhoods characterized by different distances from the coastline, building density, vegetation cover, and land use. Second, satellite-derived land surface temperature (LST) from ECOSTRESS was employed to provide a spatially continuous view of surface thermal patterns at different times of the day. Third, the recently developed offline MLUCM BEP+BEM urban canopy model (Pappaccogli et al., 2025) was applied and evaluated against observations as a science-based tool for representing intra-urban thermal variability under realistic mesoscale forcing. Observations reveal a pronounced coastal–inland gradient in both air temperature and humidity, particularly during daytime, driven by the onset and persistence of sea-breeze circulations. Coastal locations experience moderated warming and higher humidity, whereas inland districts exhibit stronger heating and daytime drying, amplifying thermal stress. Satellite LST confirms these patterns, highlighting persistent hotspots in dense urban fabrics and large impervious areas, while also capturing the diurnal evolution of surface thermal contrasts. Model results demonstrate that MLUCM BEP+BEM improves the representation of intra-urban variability compared to reanalysis data alone, particularly in reproducing canopy-level temperature differences across neighborhoods. While mesoscale forcing largely controls the background climate signal, microscale processes associated with urban geometry, surface properties, vegetation, and anthropogenic heat contribute substantially to spatial variability and are effectively captured by the model. The relative importance of these contributions varies with distance from the coastline and the choice of boundary forcing. Overall, this work highlights the necessity of integrating observations, remote sensing, and urban canopy modeling to accurately characterize thermal environments in Mediterranean coastal cities. The proposed framework is transferable to other coastal contexts and provides a robust basis for assessing urban heat exposure and for the development of urban climate services that support climate-sensitive planning and the evaluation of mitigation and adaptation strategies under current and future climate conditions. This work is supported by ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by European Union – NextGenerationEU (CUP F83C22000740001).ReferencePappaccogli, G., Zonato, A., Martilli, A., Buccolieri, R., and Lionello, P.: MLUCM BEP + BEM: an offline one-dimensional multi-layer urban canopy model based on the BEP + BEM scheme, Geosci. Model Dev., 18, 7129–7145, https://doi.org/10.5194/gmd-18-7129-2025, 2025.

Street trees can significantly reduce urban heat through shading and transpirative cooling, but their effectiveness may be compromised during heat waves with dry soil conditions. This study assesses the cooling potential of street trees using a modified BEP-Tree model incorporating soil moisture dynamics and thermos-physiological indices like the Universal Thermal Climate Index (UTCI). The model is first validated with a micrometeorological measurement campaign during hot summer days in a town in the Metropolitan Area of Barcelona, Spain, yielding a good model performance representing the spatial variability of the mean radiant temperature and UTCI inside the studied neighbourhood. Then, BEP-Tree is used to simulate two highly-compact neighbourhoods with differing tree densities in the city of Barcelona, combined with varying soil moisture levels during a heat wave. Results show that street trees in moist soil reduced air temperatures by an average of 1.2 ± 0.4 °C, leading to UTCI reductions up to 3.2 °C. Albeit air-cooling effects were reduced under dry-soil conditions, shade-driven temperature reductions remained substantial, retaining 71–97% of the cooling effect. The findings highlight the importance of tree shading rather than air-cooling in maximising the cooling capabilities of street trees during extreme heat events. Additionally, it is crucial to maintain adequate soil moisture to preserve tree health and substantial foliage to provide consistent microclimatic benefits, particularly during periods of strong heat stress.

Rapid urbanization and climate change have intensified the need for accurate urban microclimate modelling tools to support sustainable urban planning and mitigate adverse environmental impacts. Models capable of simulating the complex interactions between urban surfaces, buildings, and vegetation are essential for assessing the effects of climate change, urban overheating, and energy consumption. The MLUCM BEP+BEM model introduces advancements in urban microclimate modelling by integrating enhanced turbulent diffusion schemes with the Building Effect Parameterization (BEP) and the Building Energy Model (BEM). The model incorporates updated turbulent length scales and eddy diffusivity coefficients that account for atmospheric stability, as well as a representation of urban vegetation, including green spaces and street trees. Designed for offline operation, it offers low computational cost, making it suitable for standalone use, coupling with climate projections, and conducting long-term simulations to assess the effects of different emission scenarios on urban environments. Validation against observational data from the Urban-PLUMBER project, conducted at a suburban site in Preston (Melbourne, Australia), demonstrates reliable performance in simulating upward shortwave (SWup) and longwave (LWup) radiation. Sensible heat flux (Qh) and momentum flux (Qtau) are also accurately reproduced, highlighting the model’s robustness in complex urban environments. An underestimation of latent heat flux (Qle) suggests that further investigation and refinement of the representation of moisture-related processes in the model would be beneficial. The adaptability of the MLUCM BEP+BEM model enables its application across various climatic contexts to evaluate the impacts of climate change on urban heat stress, energy demand, and the effectiveness of adaptation strategies. Potential applications include analysing green roofs, cool roofs, photovoltaic systems, and other mitigation measures to support sustainable urban development.This work is supported by ICSC – Centro Nazionale di Ricerca in High Performance Computing, Big Data and Quantum Computing, funded by European Union – NextGenerationEU (CUP F83C22000740001).

Recently, air quality has become a major concern for policy makers around the world, which has led to the implementation of mitigation measures. In urban areas, most measures affect the road transport sector, as this is one of the main contributors to air pollution in those areas. Due to the fact that spatial variability of urban air pollution is very heterogeneous, high spatial resolution modelling is necessary. In this context, this study aims to evaluate the air quality impacts of several measures applied to the traffic network around a real urban hot-spot (Plaza Elíptica, Madrid) at high spatial resolution. The methodology used is based on Computational Fluids Dynamics (CFD) modelling, but uses different modelling tools to obtain all the necessary input data. The SUMO microscopic traffic simulator is used to obtain a dataset of traffic flows for each scenario selected in the study. This dataset represents the regular traffic during the week, avoiding the cost of computational time and resources to run simulations for each hour. Emissions are computed for each timestep of the simulations (0.75 s) using the emissions model PHEMLight5 coupled with SUMO. A set of steady-state CFD simulations are previously performed for all wind direction sectors and scenarios, using the previously described emissions. The horizontal spatial resolution of these simulations is of 5x5 m2, which higher resolutions (up to 1x1 m2) near buildings. Relevant meteorological variables are obtained from WRF simulations using the urban parameterization BEP-BEM. These are necessary for both selecting the appropriate CFD simulations from the dataset according to the observed wind direction at each hour and estimating NOx maps from pre-calculated CFD simulations based on the wind speed observed at each hour. Finally, background NOx concentrations are obtained from an urban background air quality monitoring station (AQMS) in Madrid, located 1.6 km NW from the AQMS of Plaza Elíptica. Using this methodology, we have studied four scenarios: Base scenario. (Year 2016) Reorganization of traffic flows by changing traffic directions in some streets. (Year 2019) The initial phase of the implementation of a Low Emissions Zone (ZBE) affecting the most polluting vehicles; with still some reduction of traffic due to the COVID-19 pandemic. (Year 2022) The recovery of traffic after the COVID-19 pandemic. (Year 2023) Results were evaluated for February 2016, 2019, 2022 and 2023 using the observed concentrations at the AQMS in the study area. The impacts of the traffic variations are investigated for different meteorological conditions.

Environmental stressors pose significant threats to human well-being, especially in urban areas. While extensive research—particularly in the United States—has documented higher exposures in socially vulnerable neighborhoods, studies that examine and compare residents’ perceptions of multiple environmental risks, while also accounting for actual exposure levels and neighborhood characteristics, remain relatively more limited. Our study addresses this gap by examining perceptions of residential outdoor environmental quality, integrating objective data on built and social environments with multiple measures of pollution, heat stress, and noise, alongside an original spatially referenced survey of Barcelona residents. Consistent with previous research, our results reveal distinct levels of concern among the three environmental risks, with heat stress evoking the highest level of concern. Only the concern about noise aligns significantly with measured exposure at the census tract level. By contrast, air pollution and heat stress risk perceptions are more strongly shaped by political ideology, environmental concern, and place attachment. Respondents living in wealthier neighborhoods report higher concern about heat waves, regardless of their actual levels of exposure to heat stress. The evidence suggests that perceptions of urban environmental risks are socially and spatially patterned, and not always proportional to actual exposure.

The proportion of the world’s population living in cities has increased from 37% to 56% over the last 50 years, and it is expected to continue rising further to 60% by 2030 (UN, 2022). As an essential effect of this evolution, urban land cover has expanded rapidly. In the case of Madrid, the increase in urban fraction during the period from 1970 to 2020 has been 20%. It is well known that urbanization reduces the vegetated cover and modifies surfaces properties altering the surface-atmosphere interactions and the different terms of the Surface Energy Balnace (SEB) compared to nearby rural areas. Therefore, analyzing the influence of these changes in urban land cover contributes to understand the potential risks that urban residents might face considering the urban grown and the expected temperatures increases, as this has adverse impacts on human health, livelihoods, and key urban infrastructure. The aim of the present study is to examine the consequence of Madrid's urban growth on the near-surface air temperature and on the SEB. We conduct a modeling study using WRF-ARW with the multilayer urban parameterization BEP-BEM, in which the land use and the land cover have been modified according to urban expansion in Madrid and its surroundings from 1970 to 2020. Two scenarios of common meteorological conditions of special interest are selected for this study: a period of intense heatwave during the summer season and a short period of strongly stable atmospheric conditions in winter, both observed in 2020. The results show that in areas where the urban fraction become greater an increase in near-surface air temperature is found for both simulated periods, especially during the night, pointing out that the cooling rate decreases in urban areas. The growing of urban land cover over time also modifies the SEB and turbulent transport in Madrid and surroundings, leading to an increase in temperatures, specially for the minima ones.

This study investigates the Urban Heat Island (UHI) effect in Grenoble, France, during the August 2018 heatwave, using high-resolution Weather Research and Forecasting (WRF) simulations at 111 meters. The objective is to evaluate at this resolution the capac- ity of different WRF urban parameterizations such as the Building Effect Parameterization (BEP) and Building Energy Model (BEM), to simulate the UHI effect and overall temper- ature distribution. The validation approach integrates data from official weather stations, crowdsourced Citizen Weather Stations (CWS), and empirical and modeling studies of the UHI in Grenoble. Results show that configurations with advanced urban parameterizations significantly enhance the ability to capture the spatial structure of UHI in urban areas while maintaining strong performance in non-urban regions. However, a trade-off was identified: models that accurately capture the spatial distribution of UHI often exhibit larger errors in absolute temperature predictions, particularly at individual stations. Additionally, all config- urations struggled to simulate thermal wind reversals, a key process affecting UHI dynamics in Grenoble’s valley. This work highlights the importance of advanced urban parameteriza- tions in improving UHI modeling in complex urban and topographic settings.

Escalating urban heat, driven by the convergence of global warming and rapid urbanization, is a profound threat to billions of city dwellers. The science directing urban heat adaptation is strongly influenced by studies that use satellite-based land surface temperature (LST), which is readily available globally and address data gaps in cities, particularly in the Global South. LST, however, is a poor surrogate for near-surface air temperature, physiologically relevant human thermal comfort, or direct human heat exposure. This flawed practice leads to issues for several downstream use cases by inflating adaptation benefits, distorting the magnitude and variability of urban heat signals across scales, and thus misguiding urban adaptation policy. We argue that satellite-based LST must be treated as a distinct indicator of surface climate, which, though relevant to the urban surface energy budget, can be frequently decoupled from human-relevant thermal impacts especially during daytime. Only by a disciplined application of this variable, combined with complementary datasets, process-based and data-driven models, as well as interdisciplinary collaboration, can urban adaptation design and policy be effectively advanced.

As climate change continues to exert an impact on urban areas, the comprehension of its effects on the urban environment becomes crucial for sustainable urban planning. This study presents a novel approach employing the Building Effect Parameterization (BEP) coupled with a Building Energy Model (BEM) in an offline configuration to simulate urban climates. The multi-layer BEP+BEM model, properly describes the vertical arrangement of urban fabric, accounting for the distribution of heat, moisture, and momentum sources throughout the urban canopy layer. Additionally, energy consumption within buildings for both cooling and heating is estimated by the BEM, providing a comprehensive perspective on the urban energy balance. Coupled with a 1-D column model of urban canopy flow, the BEP+BEM offline model accurately estimates drag coefficients and turbulent length scales based on urban fabric characteristics. In the proposed version, the model has been extended to consider additional factors such as green areas and street trees, along with existing green roofs, photovoltaic panels and the permeability of urban materials. This expansion enhances the model's capability to assess the effectiveness of sustainable infrastructure in mitigating climate change effects on urban areas. In this study, the BEP+BEM scheme is forced by data from climate projections, allowing for the dynamic representation of various Local Climate Zones (LCZs) under distinct climatic conditions. Simulations in different LCZs and under different climatic conditions are compared to evaluate the impact of climate change on urban environment, enabling the exploration of how different urban areas respond to changing meteorological forcings. The sensitivity analysis includes a range of standard urban typologies (i.e. LCZs), capturing the complexity of interactions between the built environment and the atmosphere. This approach offers an assessment of the impacts of climate change on key urban phenomena, such as urban heat islands (UHI), thermal discomfort, and heightened energy consumption by buildings. The outcomes of this study provide valuable insights for the urban climate community, policymakers, and researchers with the aim of enhancing the resilience of cities in the face of a changing climate. By bridging the gap between climate projections and urban climate simulations, a consistent framework is presented in this work for evaluating and adapting various urban environments to future climatic conditions.