The increasing frequency and intensity of extreme weather events have posed new challenges to the sustainability of agriculture in Southeastern Brazil. This study analyzes agricultural climate risk from an integrated perspective, combining the Temperature and Humidity Index (THI) — representative of thermal stress — and the Standardized Precipitation and Evapotranspiration Index (SPEI-12) — an indicator of the severity of meteorological droughts. Historical monthly series from 2003 to 2023 were used to identify atypical months with the highest climate risk, based on the maximum values of the SPEI and critical percentiles of the THI (P95). The analysis resulted in panels of monthly anomalies and a composite index of extreme climate risk, weighting the effects of drought and heat on the territory of the Southeast. The results show that the most affected regions include Northwest São Paulo, part of the Triângulo Mineiro, North-Central Minas Gerais, Zona da Mata, the Rio Doce Valley, and Northern Espírito Santo. The methodology applied was satisfactory for identifying regional climate extremes and represents a valuable tool to support the formulation of public policies, offering technical subsidies for productive and territorial resilience in the face of global climate change. Objective: The objective of this study is to propose an integrated analysis of the ITU and SPEI indices applied to the Southeast region of Brazil, from 2003 to 2023, identifying spatial and monthly patterns of agricultural climate risk associated with extreme heat and drought events. Theoretical Framework: Climate change has resulted in a new risk configuration, characterized by the increased frequency and severity of droughts, heat waves, and compound events of water and heat stress. This reinforces the urgency of climate adaptation strategies in the agricultural sector. The Southeast region of Brazil presents significant climatic and physiographic diversity, with interannual and decadal variability in rainfall and temperatures intensifying, resulting in simultaneous drought and heat anomalies, impacting crops and livestock. Method: The methodology is based on the application of the Standardized Precipitation and Evapotranspiration Index (SPEI-12) and the Temperature and Humidity Index (THI). The index data were standardized and spatially normalized to generate a composite metric for climate risk analysis. Results and Discussion: The results contribute to public policies aimed at agricultural adaptation strategies in the face of global warming, providing technical support for territorial management, agricultural planning, and mitigation of production losses in a context of increasing climate uncertainty. Research Implications: This approach aims to understand intra-annual variability and highlight periods of overlapping heat and drought, which are fundamental for planning adaptive strategies in the agricultural sector of Southeastern Brazil. Originality/Value: The relevance of this research lies in its methodological and practical contribution to the assessment of the combined risks of heat and drought from a historically and spatially detailed perspective.

Abstract Extreme heat, an increasing cause of weather-related deaths worldwide, poses escalating risks to both urban and rural communities. While urban heat and its impacts have received scholarly and practitioner attention for decades, rural heat has tended to be overlooked. To address this, we conducted a systematic literature review using Scopus, yielding 52 articles specifically addressing extreme heat in rural communities in the United States, Canada, and Australia. This review synthesizes the impacts of extreme heat on rural communities and current efforts and challenges in addressing rural heat risks. Among 52 articles, about three-quarters focus on the impact of extreme heat on public health across diverse groups. Key findings include: (1) outdoor workers (e.g., farmers) face particularly high risks due to prolonged exposure to heat; (2) the elderly, Indigenous people, and visitors in rural regions are also vulnerable to extreme heat; (3) rural heat risks are often shaped by the intersectional vulnerabilities of rural communities and governance gaps, such as inadequate or missing regulations to protect workers; and (4) compared to urban heat governance, rural heat governance remains under-developed in many areas, highly fragmented, and inconsistent across regions, which leaves vulnerable populations more exposed. We use a heat mitigation (e.g., home weatherization), management (e.g., occupational heat safety standards), and heat governance (i.e., policies, and actions taken by governments, institutions, and communities) framework to identify gaps in current approach and discuss future research direction towards an integrated and robust approach to increase rural heat resilience.

Modeling urban microclimates is essential for assessing thermal comfort and the urban heat island (UHI) effect, particularly in the context of climate change. The UHI intensifies thermal discomfort, increases energy demand, and exacerbates health risks during extreme heat events. Accurate urban modeling is crucial for evaluating microclimatic conditions and developing effective mitigation strategies. However, traditional 3D modeling approaches often lack the efficiency and precision required to capture complex urban morphologies and integrating key environmental elements such as vegetation. This study presents an optimized workflow for large-scale 3D urban modeling that combines open-source geospatial data with programming and parametrisation tools to enhance the accuracy and scalability of urban studies. The methodology applied in Seville comprises data acquisition, processing, and modeling to produce a high-resolution urban environment model. Using Grasshopper and the ShrimpGIS plugin, spatial datasets of buildings and urban vegetation are processed to create a high-fidelity model. The resulting model is structured for integration into environmental analysis tools such as Ladybug Tools. This integration enables the direct assessment of design choices and morphological relationships for climate resilience, facilitating a detailed evaluation of urban microclimates and climate adaptation strategies. This approach provides urban planners and researchers with a replicable, efficient methodology to support evidence-based decisions for climate-responsive urban development.

Abstract. Soil moisture–temperature coupling (SM–T) significantly influences the frequency and amplitude of heat extremes. It describes how variations in soil moisture affect surface air temperature conditions and vice versa. This study aims to determine the spatial extent and duration of SM–T in southern and central Sweden, an area increasingly recognized as a coupling hot spot, during the extreme warm conditions between May and August 2018 (MJJA 2018). The assessment of coupling is based on a multi-correlation overlay analysis of key coupling variables: surface soil moisture, evaporative fraction, and daily maximum 2 m temperature from four different simulations of the coupled regional climate model WRF-CTSM, along with a merged gridded GLEAM-E-OBS observational–reanalysis dataset. These datasets demonstrate robust precision in representing the magnitude and variability of the key coupling variables during the MJJA 2018 compared to in situ observations, though the precise timing and duration of the coupling are challenging to reproduce at the local scale. WRF-CTSM provides a more realistic depiction of the key coupling variables and their interactions when recent CTSM advancements are incorporated. On average, across the study region and all five datasets, SM–T persisted for 22 d throughout the MJJA period. The atmospheric leg alone (involving daily evaporative fraction and maximum 2 m temperature), averaged across datasets, contributed 92 % to the regional coupling duration.

The temporal change in heat-related mortality in relation to the Heat Health Action Plans in the five major cities of Australia.

Always on duty - Fostering climate resilience in the nursing profession: A discussion paper.

Extreme heat during Hajj – Countermeasures to prevent mortality and ensure wellbeing

The dual impacts of built environment on extreme heat and urban heat resilience: A comparative study in Beijing

Effect of ambient temperature and thermal comfort indices on emergency visits for psychotic and mood disorders: a case-crossover study in a French suburban area.

A systematic review assessing the association between extreme temperature exposure and cardiovascular health outcomes in Africa.

Drivers of day–night intra-surface urban heat island variations under local extreme heat: A case study of Singapore

Associations between PM2.5 exposure and birth outcomes and effect modification by extreme heat events during pregnancy in a North Carolina cohort

Enhancing ICT utilisation for urban extreme heat adaptation: Perspectives from Southeast Asia

Advancing urban extreme heat modeling with spatial machine learning ensembles: A case study for providence, Rhode Island

Effects of temperature changes due to climate change on human health

Understanding Heat Exposure Risks and Adaptation Behaviors Among Elderly Asian Communities in New York City.

Extreme heat triggers first-time acute myocardial infarction: Evidence from a case-crossover study in Tianjin, China

Abstract The Australian Community Climate and Earth-System Simulator–Modified Initial Condition Attribution System (ACCESS-MICAS) is a forecast-based numerical modeling tool used to understand the causes of multiday to seasonal climate events by altering states in the forecast initial conditions and forcing. Built on the Bureau of Meteorology’s operational global coupled subseasonal to seasonal forecast system, ACCESS-S2, we reforecast an event with hypothetical atmosphere, land, and ocean initial conditions and/or external forcings. The system is primarily designed to assess the potential contribution of anthropogenic climate change to the magnitude of a weather event or climate extreme but can be applied more generally to assess the sources of predictability or response to an imposed anomaly. This paper describes the system with a specific focus on the climate change attribution configuration. We examine key technical aspects, including the required spinup time, the presence of initialization shock, and the model’s ability to represent an altered climate, including dynamical changes. The system is applied to the 2019–20 Australian Black Summer, focusing on the role of ocean state in modulating temperature and rainfall and the influence of anthropogenic climate change on heat wave intensity and fire weather conditions. Results show that the positive Indian Ocean dipole increased the temperature and suppressed rainfall in northwest Australia, exaggerating the fire danger in the region, while climate change contributed significantly to the severity of heat extremes and fire danger, with the event being even more extreme in the future climate. These results demonstrate the utility of ACCESS-MICAS for both attribution and broader climate diagnostics.

Abstract Extreme heat poses a growing public risk, yet understanding real-time, population-level human response remains a major challenge. This study leverages Google Trends search activity (2016–2024) across 30 major US metropolitan areas to quantify digital responses to heat. Using generalized additive models, two key response metrics are identified: a temperature ‘threshold’ that triggers public concern and a ‘slope difference’ that measures the intensity of the subsequent reaction. The analysis first establishes that a city’s median climate primarily determines its baseline threshold, while the variability of climate governs the slope difference, both reflecting long-term adaptation to local conditions. After accounting for these climatic effects, the study finds that socioeconomic factors explain a significant portion of the remaining variance in digital heat responsiveness. Specifically, the climate-adjusted threshold is higher in cities with greater social vulnerability, indicating a dangerous delay in awareness. This interplay creates five distinct urban typologies, revealing that temperate coastal cities exhibit a highly reactive pattern while arid inland cities show a response buffered by modern infrastructure. These findings uncover a critical disconnect wherein the populations most at risk are often the last to digitally engage with the threat. This digital proxy provides a new, near real-time method for monitoring public risk perception, offering a vital resource for developing more equitable public health interventions against the escalating threat of extreme heat.

ABSTRACT Compound extreme events, including extreme wet conditions and extreme heat, exhibit distinct temporal sequences, yet their characteristic differences remain systematically understudied. In this study, we attempt to reveal the main types and dominant factors affecting compound rainstorm and heatwave (CRH) events in the southern region of China. Firstly, we define CRH events and then classify them into two categories (binary and multiple) according to the number of individual events. Then, we analyze the spatiotemporal changes in the frequency of each kind of CRH event during the period from 1981 to 2020. Finally, the contribution of heatwaves and rainstorms to CRH events and to the frequency change is assessed. The results show that CRH events mostly occurred in southeast China and eastern Sichuan. The contribution of heatwaves is larger for most multiple CRH, while heatwaves and rainstorms contributed equally for most rainstorm–heatwave events. In addition, the frequencies of binary CRH events increased rapidly after 2000. Heatwaves are the main dominant factor leading to the rapid increase in the frequency of CRH events. The results help improve our understanding of the combination of rainstorms and heatwaves and provide a justification for studying compound climatic hazards.