Physical Drivers of the November 2023 Heatwave in Rio de Janeiro

Physical Drivers of the November 2023 Heatwave in Rio de Janeiro | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Physical Drivers of the November 2023 Heatwave in Rio de Janeiro Catherine C. Ivanovich, Adam H. Sobel, Radley M. Horton, Ana M. B. Nunes, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5355924/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract As extreme heat has not historically been a major hazard for the city of Rio de Janeiro, the November 2023 Heatwave magnitude and timing were staggering. Here we conduct a case study of reanalysis data and high-resolution projections to explore the event drivers and characterize the evolving extreme heat risk in the city of Rio de Janeiro. We find that the heatwave was associated with atmospheric blocking, potentially linked to the 2023-24 El Niño event. Soil moisture declines increased surface sensible heat flux, and elevated sea surface temperatures reduced coastal cooling. The heatwave was preceded by weeks of suppressed precipitation and terminated by the onset of rain. We also find a significant historical increase in the frequency of high heat days throughout Brazil and a lengthening of the heat season in the city of Rio de Janeiro. The frequency of the city’s austral spring heat extremes is expected to increase further in the future, highly dependent upon our future emissions pathway. These results emphasize the rapidly emerging risk for extreme heat in the city of Rio de Janeiro. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Climate sciences/Atmospheric science Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics Scientific community and society/Social sciences/Climate change/Climate and earth system modelling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction In the spring of 2023, the city of Rio de Janeiro experienced a high impact heatwave that caught the world’s attention. Media sources ranging from local reporting to international news companies centered stories on the event’s record-breaking temperature magnitudes and unseasonal timing, arriving earlier in the warm season than typical heatwaves (Correio Braziliense, 2023 ; Hughs and Jeanet, 2023). The impacts of the extreme heat were widely publicized in part due to the tragic death of a concertgoer hospitalized during a Taylor Swift performance in Rio de Janeiro on November 17, with news articles reporting heat-induced cardiovascular distress as the cause of death (Nguyen, 2023 ). Sources also report that the stadium in which the concert took place experienced higher temperatures than those measured in the open air, as well as a lack of cooling equipment and insufficient water for attendees (Nguyen, 2023 ; Jornal Nacional, 2023 ). Such complexities highlight that extreme heat experienced by individuals on the ground can far exceed temperatures measured at local weather stations, depending on infrastructure and the capacity for cooling interventions (Wilby et al., 2021 ; Nahlik et al., 2017 ). However, the meteorological event itself is of course one of the preconditions for societal impacts. We therefore explore the physical mechanisms behind the heatwave as one key step towards improving preparation for the impact of future extreme heat events. Throughout Brazil, the highest temperatures occur climatologically in low latitude and low altitude regions in the interior of the country, such as the cities of Teresina (Piauí, in the Northeast region of Brazil) and Palmas (Tocantins, in the Central-West region of Brazil; Alvares et al., 2013 ), both of which are far from Rio de Janeiro. Extreme temperatures tend to be intensified by land-atmosphere interactions, as dry soils partition more energy into sensible heat (Geirinhas et al., 2018 ). These relationships between the atmosphere and land surface processes increase the likelihood of compound extreme heat and drought events and intensify impacts on agriculture (Cirino et al., 2015 ), worker productivity for outdoor laborers (Bitencourt et al., 2021 ), wildfire risk (Libonati et al., 2022 ), and direct impacts on human health (Zhao et al., 2019 ). As the frequency and intensity of extreme heat throughout Brazil has increased significantly in the past decades and is projected to continue in the future (Feron et al., 2019 ; Regoto et al., 2021 ; Bitencourt et al., 2020 ), the widespread socio-economic impacts of these events are likely to grow. While Rio de Janeiro is the second most populous city in Brazil (Instituto Brasileiro de Geografia e Estatística 2022 ) and the third most populous city in South America (United Nations Department of Economic and Social Affairs Population Division 2022 ), few studies have focused on extreme heat in the city. On one hand, Rio de Janeiro has not historically been a major hotspot of extreme heat in Brazil and has experienced fewer heatwaves relative to other major cities in the country (Geirinhas et al., 2018 ). Further, the numerous microclimates within the city, influenced by its coastal setting and complex topography, complicate the study of local heatwave dynamics. Indeed, there is large spatial variability in temperature extremes across the Rio de Janeiro metropolitan area compared to other Brazilian cities (Alvares et al., 2013 ). However, impactful heatwaves in recent decades have increasingly drawn attention from public health officials and scientific communities alike. Recent literature has explored the dynamics and mortality impacts of extreme temperatures during heatwaves in 2010 (Geirinhas et al., 2019 ) and 2013/2014 (Geirinhas et al., 2022 ), and work has begun to investigate compound heatwave and drought events throughout Southeast Brazil (Geirinhas et al., 2021 ). There is also building evidence that temperature extremes are increasing in intensity and frequency throughout Brazil, including the city of Rio de Janeiro (Regoto et al., 2021 ; Bitencourt et al., 2019). Climate variability also plays an important role in modulating temperatures over this area, including large scale modes of climate variability such as the El Niño-Southern Oscillation (Rehbein and Ambrizzi 2023 ; Cai et al., 2020 ; Shimizu and Ambrizzi 2015), the Pacific Decadal Oscillation, and the Atlantic Multidecadal Oscillation (He et al., 2021 ). Should more intense, frequent, and unseasonably early extreme heat events take place in the future in the city of Rio de Janeiro, these heatwaves may have increased impacts on human health due to potential exceedance of unprecedented temperature thresholds and individuals’ lack of preparation for these events. In a tropical city where baseline temperatures are already relatively high, small shifts in the temperature distribution can have large impacts on the frequency of extremes (Cheng et al., 2019 ), particularly at thresholds relevant to human health outcomes (Vecellio et al., 2022 ). These health risks are compounded by the humidity in Rio de Janeiro, a coastal city with ample moisture sources from the ocean and surrounding vegetation, priming the region for humid heat extremes which are physiologically more dangerous to human health than dry heat (Mora et al., 2017 ). In this study, we explore the meteorological conditions that led to the extreme heat event in November 2023 in the city of Rio de Janeiro. We identify drivers of the exceptional magnitude and persistence of the extreme temperatures, as well as their early arrival in the calendar year. We compare these conditions to those associated with typical heatwaves in the region, and particularly events taking place in the spring season. We then consider how extreme spring temperature events have shifted throughout the historical period, and how we might expect them to change in the future with ongoing anthropogenic climate change. Methods 2.1 Data This analysis employs both station-based observations and reanalysis data. Initial analyses are conducted on subdaily station data from the city of Rio de Janeiro, accessed via the Met Office Hadley Center’s HadISD station-based dataset (Dunn 2019 ) and the Rio Alert System produced by the Rio de Janeiro City Hall (Sistema Alerta Rio da Prefeitura do Rio de Janeiro 2024 ). Three airport weather stations are available from HadISD for the city of Rio de Janeiro, namely the Galeão/Antonio Carlos Jobim International Airport (located on the island Ilha do Governador within the Guanabara Bay), the Campo Délio Jardim De Mattos Airport (an Air Force base located in the city’s North Zone), and the Santos Dumont Airport (a waterfront airport located near the city center). Six additional stations from the Rio Alert System dataset record measurements from the top of various community and commercial buildings, including hotels, schools, and warehouses. These stations are located in distinct areas of the city, whose topographical and coastal complexities contribute to various microclimates. Weather and climate recorded by each of these stations is thus slightly distinct (see Fig. S1), which is a challenge that has been previously identified in the literature (Lyra et al., 2018 ; Dereczynski et al., 2013 ). We therefore base the majority of our analysis on reanalysis data and compare the identified patterns with station data when possible. This comparison is particularly important for extreme events, as the magnitude of extreme heat has been shown to be biased in reanalysis products due to their spatial and temporal smoothing of observations (Rogers et al., 2021 ; Raymond et al., 2020 ). Further, the human experience of heat stress is inherently hyperlocal, meaning that the distinct microclimates existing throughout the city can control heat stress exposure and the efficiency of adaptation strategies. However, the present study is primarily concerned with the regional drivers of the extreme event rather than its absolute magnitude. Reanalysis provides continuous spatial coverage and a wide array of internally consistent meteorological variables, which warrants its use for the application here. Hourly meteorological data are retrieved from the fifth major global reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5), including 2-meter temperature, 2-meter dewpoint temperature, volumetric soil water for layer 1 (0–7 cm, where the surface is at 0 cm), surface pressure, geopotential height at 500 hPa and 200 hPa, precipitation, evaporation, 2-meter horizontal winds, and vertical velocity at 500 hPa (Hersbach et al., 2020 ). From this hourly data, daily maximum temperature, daily total precipitation, and daily means of all other variables are calculated from 1979–2023. Daily mean sea surface temperature (SST) data from 1979–2023 is also retrieved from the NOAA 1/4° Daily Optimum Interpolation Sea Surface Temperature (OISST) dataset (Huang et al., 2021 ). We also explore the future evolution of temperature extremes over the city of Rio de Janeiro using the NEXGDDP dataset (Thrasher et al., 2022 ). This data product is statistically downscaled from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models, with a spatial resolution of 0.25 degrees and outputs variables on a daily temporal scale. We directly retrieve daily maximum temperature data through the end of the century under the Shared Socioeconomic Pathways (SSPs) SSP2-4.5 and SSP5-8.5 for the 23 models which output this variable and pair of scenarios for each day in the calendar year through 2100. Because of Rio de Janeiro’s complex coastal and mountainous terrain, projections data may not accurately capture fine scale differences in the city’s climate. For example, recent literature has shown that the coastal cooling relative to inland areas experienced in regions such as the eastern United States may be underestimated by models (Raymond and Mankin 2019 ). However, models are particularly biased in regions with large land-ocean surface temperature contrasts, and Rio de Janeiro’s location as a tropical city and the fact that the extreme events analyzed in this study take place in the spring when this temperature gradient should be relatively small suggest that these biases may be muted compared to other regions and seasons. In order to address these potential sources of error, we generate a set of synthetic time series based on NEXGDDP projections which retain the seasonality and variability recorded in the historical reanalysis data from ERA5. We use a percentile matching technique in which we first bin all data for the grid cell which includes the city of Rio de Janeiro during a historical base period (1981–2013) into one-percentile bins for both the NEXGDDP and ERA5 datasets. We additionally bin all NEXGDDP data from this gridcell into one-percentile bins during one midcentury period (2041–2060) and one end-of-century period (2081–2100). We then calculate the temperature delta for each percentile bin between the base period and both the midcentury and the end-of-century periods in the NEXGDDP data. Finally, we apply these percentile specific change factors to each associated bin in the historical ERA5 base period. 2.2 Methodology We first create time series for the historical day-of-year climatologies of variables in the city of Rio de Janeiro and compare them to the evolution throughout 2023. All anomalies are calculated relative to historical mean calendar date values (i.e., the daily maximum temperature anomaly on November 18, 2023 is calculated by subtracting the mean daily maximum temperatures on November 18 in all previous years in the historical record from the recorded absolute magnitude of the event). We also generate maps of concurrent meteorological variables relevant to the extreme heat event for the greater region outside of Rio de Janeiro. We compare these spatial patterns to those experienced during previous extreme heat events in Rio de Janeiro, calculated as 99th percentile daily maximum temperature days across all seasons for the grid cell which includes the Galeão International Airport weather station. We then select for events which only occur in the September-November (SON) austral spring season. We also quantify how extreme heat in the city of Rio de Janeiro is shifting using a variety of methods. We first consider how the frequency of extreme temperatures is changing over time in Brazil and define these extreme temperatures using both absolute and relative thresholds. We select these thresholds as 30°C and the locally defined 90th percentile daily maximum temperature at each grid cell. These thresholds are chosen in order to investigate impactful temperature magnitudes while ensuring sufficient sample size for the trend analysis. We also visualize the broadening of the extreme heat season, calculated based on the number of days between the start of the first heatwave and end of the last heatwave of the season. A heatwave is defined here as a three-day period with consecutive daily maximum temperatures above the 50th percentile of daily maximum temperatures across the two hottest months of the year in the city of Rio de Janeiro (January and February); this 50th percentile threshold is equal to about 31.4°C. This heat season definition is informed by a definition used by the United States Environmental Protection Agency (US EPA 2021 ), adapted to better reflect Rio de Janeiro’s lower temporal variability in temperature due to its tropical location. Finally, we calculate how spring temperature distributions have already changed in Rio de Janeiro by comparing early and late historical periods in ERA5 for the grid cell which includes the Galeão International Airport weather station. Distributions are calculated from annual spring maximum temperatures in the city of Rio de Janeiro and fit using GEV distributions, which have been shown to well capture extreme temperature distributions (Powis et al., 2023 ; Van Oldenborgh et al., 2022). For comparison, we also plot GEV distributions for early and late historical periods in the NEXGDDP model data before applying our bias-correction technique. The location parameter and spread of the model data distributions during these periods is much lower than that of ERA5 (Fig. S2), further motivating our use of synthetic time series to explore how these distributions may change in the future. We then use the bias-corrected NEXGDDP data for the 23 models which report daily maximum temperature for each day in the calendar year under the aforementioned SSP2-4.5 and SSP5-8.5 scenarios during a midcentury and end-of-century time period. We additionally evaluate the impact of only using models which most accurately reproduce the historical observed daily maximum temperature record in the city of Rio de Janeiro. We calculate the Perkins skill score to evaluate the similarity between probability density functions of daily maximum temperature in the reanalysis dataset (ERA5) and each of the 23 global climate models during the historical period. These skill scores are calculated as the cumulative minimum between the observed and modeled distributions of each binned value (Perkins et al., 2007 ). We finally select the 6 climate models which exhibit skill scores greater than 0.8, indicating that these models capture over 80% of the observed probability density functions. The result of this analysis is shown in Fig. S3, but the interpretation of the results as shown in the main text using all 23 models does not change. Results 3.1 Rio de Janeiro’s spring 2023 heatwave The city of Rio de Janeiro experienced exceptionally high temperatures in both the austral winter and spring of 2023, peaking on November 18 (Fig. 1 ). This record-breaking event became the highest daily maximum temperature on record at the Galeão International Airport weather station, reaching 41.3°C. The extreme heat event was also notable for its accompanying high specific humidity, which rose alongside temperature in the days leading up to November 18 (Fig. 1 b). The combination of elevated temperature and humidity rendered the event a humid heat extreme, as measured by wet bulb temperature (Fig. 1 c). The coincidence of extreme dry and wet bulb temperatures is typical for extreme heat events in Rio de Janeiro, where there is a statistically significant positive correlation between daily maximum temperature and daily mean specific humidity (Fig. S4). This relationship is facilitated by the city’s abundant access to moisture from the coast and surrounding vegetation. Elevated temperatures were not limited to the city of Rio de Janeiro, but were spatially constrained by elevation (Fig. 2 ). We explore the spatial patterns of the heatwave in data from the European Centre for Medium-Range Weather Forecasts (ERA5) reanalysis during the period of 1979–2023 (Hersbach et al., 2020 ). We see that hotspots in elevated temperatures were located throughout the coastal region surrounding Rio de Janeiro, with sharp declines across the mountainous terrain moving inland. These positive coastal temperature anomalies coincide with northerly surface wind anomalies. ERA5 estimates the daily maximum temperature on November 18 in the grid cell containing the Galeão International Airport weather station as 40.6°C, within the range of temperatures recorded throughout weather stations in the city (Fig. S1). This extreme event was also remarkable in length as measured by ERA5, as daily maximum temperatures were above the locally defined 90th percentile for eight consecutive days, and above the 99th percentile for the final three days of this period (percentiles calculated from ERA5 across the period from 1979–2023). This multi-day interval of exceptional temperatures rendered it difficult for residents to find relief from the heat. The maximum temperature during the event on November 18 coincided with other anomalous meteorological conditions (Fig. 3 ; for climatological values, see Fig. S5 in the Supplemental Materials). Positive geopotential height anomalies centered over Rio de Janeiro were consistent with an intensification of the South American Subtropical High, a semi-permanent anticyclonic circulation system off the Southeast coast of Brazil. The edge of this positive high pressure anomaly was collocated with the region of positive temperature anomalies that includes the city of Rio de Janeiro. Surface winds off the coast of Rio de Janeiro were anomalously northerly, while anomalous winds over the interior of South America enhanced the northerly South American Low Level Jet (Marengo et al., 2004 ; Montini et al., 2019 ). Positive specific humidity anomalies were present throughout Southeast and South Brazil, intersecting with an area of precipitation along the edge of the low pressure system to the south. The northern portion of the positive specific humidity anomaly was aligned with the positive geopotential height anomaly off the coast of Southeast Brazil. Widespread negative soil moisture anomalies occurred throughout most of Brazil, and the interior of South America more broadly, during this event. The large spatial coverage of these negative soil moisture anomalies was concurrent with Amazonian drought recorded during this time, inherited from the prior season (Espinoza et al., 2024 ). Finally, positive SSTs of up to 2°C occurred along Rio de Janeiro’s coast. These spatial patterns are typical of extreme heat events during the spring season in the city of Rio de Janeiro, though the magnitudes of the anomalies in all of these variables are dramatically higher on November 18, 2023 than during other spring extreme heat events (Fig. 4 ). The most unique features of the November 18 event were the intensified northerly winds and the degree of inland penetration of positive specific humidity anomalies. Further, the positive local SST anomalies off the coast of Rio de Janeiro were particularly exceptional in intensity and spatial scale during this event, weakening the sea-air temperature contrast and sea-breeze. Outside of these specific distinctions, the event on November 18, 2023 was an intense example of a typical spring extreme heat event in the region. The time evolution of these variables throughout the month of November 2023 uncovers the temporal development of the extreme heat event (Fig. 5 ). Rising temperatures throughout the weeks leading up to November 18 were preceded by elevated geopotential heights at 500 hPa and associated atmospheric subsidence. This was accompanied by a rapid decline in soil moisture which was likely facilitated by the increased solar insolation associated with the persistent high pressure system and resulting extremely low precipitation from November 2-November 18. Given that the rainy season in Southeast Brazil typically begins in late-October to mid-November (Coelho et al., 2021; Latinovic et al., 2018; Marengo et al., 2012 ; Liebmann and Mechoso 2011; Raia and Cavalcanti 2008 ), this period of consecutive dry days was unusual. Indeed, this period totals 17 days in a row with less than 5 mm of rain per day, and this only happened during the month of November in one other year in the historical record from ERA5 between 1979–2023 (2012). There was also a gradual increase in SST off the coast of Rio de Janeiro, though delayed compared to that of the local air temperature. Wind direction was highly variable on a daily scale, but became increasingly northerly during this same period. As air temperatures rose, specific humidity increased over the city. This was likely related to both local evaporation from the soil (co-occurring with declining soil moisture) and moisture advected from the coast and surrounding vegetation. The circulation specifically on November 18 directed wind in the larger region surrounding Rio de Janeiro to intensify the South American Low Level Jet, which can additionally increase moisture transport from the Amazon Basin to Southeast Brazil (Marengo et al., 2004 ; Vera et al., 2006 ; Montini et al., 2019 ). However, the surface moisture flux convergence was only stronger than the climatology in some grid cells within the northern and western areas of the city (Fig. S6). More generally, specific humidity was also able to build without reaching saturation due to the increasing temperatures (and the Clausius-Clapeyron relation). Finally, the heatwave was terminated when a two-day precipitation event occurred from November 19–20. This precipitation induced a small decline in specific humidity and SST, as well as a rapid increase in soil moisture. The evolution of the 2023 heatwave as shown above is reminiscent of that during the 2010 heatwave analyzed by Geirinhas and coauthors (2019). Those authors explain that the extreme heat event in the summer of 2010 was initiated by an SST anomaly over the eastern Pacific that triggered a Rossby wave train that in turn intensified the South Atlantic Subtropical High. Modulation of this high pressure system has been shown to be central to influencing weather in the city of Rio de Janeiro, and particularly temperatures there (Geirinhas et al., 2018 ). Here we also observe an SST anomaly over the equatorial Pacific throughout the month of November and a resulting anomalous wave pattern ending over the South Atlantic High that became increasingly organized and strengthened during the two weeks before November 18 (Fig. 6 ; see Fig. S7 in the Supplemental Materials for maps of the climatologies and absolute magnitudes of these variables). This mechanism is similar to how El Niño generally influences temperatures in Southeast Brazil on longer timescales (Cai et al., 2020 ), and we confirm that there is a positive correlation between the ENSO state as quantified by the Niño3.4 index and the frequency of high heat days in the city of Rio de Janeiro in the austral spring season (Fig. S8). 2023 was characterized by a transition from La Niña to El Niño conditions, with the El Niño emerging in April-June 2023 and strengthening to a strong El Niño in the second half of 2023 (Becker et al., 2024 ). The occurrence of El Niño conditions could have been responsible for initiating the wave train which set off the geopotential height anomalies over Rio de Janeiro. We note that similar wave trains driven by Pacific SST anomalies have been shown to influence weather in Southeast Brazil even during neutral ENSO states (Seth et al., 2015). Additionally, the instantaneous extreme temperature event and the preceding persistent dry conditions must also be linked to the synoptic weather in the area. Decreases in soil moisture and moisture fluxes from anomalous winds were central to the development of the heatwave in 2010, as they were in November 2023. These overlaps in the apparent drivers of the 2010 and 2023 heatwaves underscore that while last year’s spring event was unprecedented in its magnitude and unusual in its spring timing, it was not unique in its overall dynamics. 3.2 Historical and future changes in extreme heat Extreme heat events are becoming more frequent in the city of Rio de Janeiro and the timing of these events is shifting earlier in the calendar year. There has been a significant increase in the number of days above 30°C each year over the past 44 years throughout almost all of South America (Fig. 7 a). Further, the number of 90th percentile days locally defined at each grid cell has also increased significantly throughout most of the region (Fig. 7 b). In the city of Rio de Janeiro specifically, the number of 30°C days per year during the austral spring is increasing at a rate of 0.27 days/year (Fig. 7 c). Relative to 1979, the city now experiences almost 12 additional days per year above 30°C during the spring season alone. Overall, the extreme heat season in Rio de Janeiro is broadening. As measured by the number of days between the first and last heatwave day of the season (a period of three or more consecutive days with daily maximum temperatures above 31.4°C), the extreme heat season has lengthened from 156 days in the 1979–1980 season to 176 days in the 2022–2023 season (Fig. 8 ). The broadening of the heat season is due primarily to more early season heatwave days, while the end date of the heat season has not changed significantly. The distribution of maximum spring temperatures in the city of Rio de Janeiro has changed over the last four decades and is projected to continue to evolve in the future. We fit annual maximum spring temperatures from historical ERA5 reanalysis data and future projections from bias-corrected NASA Earth Exchange Global Daily Downscaled Projections (NEXGDDP) data (Thrasher et al., 2022 , see Methods) using a Generalized Extreme Value (GEV) distribution. When comparing early and late historical periods from 1979–1988 and 2014–2023, respectively, the location parameter of the two GEV distributions has increased by 1.7°C (Fig. 9 ). The distribution of maximum austral spring temperatures in the city of Rio de Janeiro is also projected to continue shifting to higher values in the future, but the magnitude of this change is strongly dependent upon the future emissions pathway. The temperature distributions associated with mid-century periods (2041–2060) under SSP2-4.5 and SSP5-8.5 future scenarios are similar to that of the last 10 years of observational data, with shifts in the location parameters of 0.1°C and 0.9°C for the two emissions trajectories, respectively. A larger change is projected by the end of the century (2081–2100) under each emissions scenario. However, the end-of-century SSP5-8.5 scenario is distinctly separate from the other distributions, with the distribution location parameter 2.8°C higher than during the last 10 years. These changes to the distributions strongly influence the probability of an event with the intensity of the maximum temperature recorded on November 18, 2023. The probability density function fit to the projected annual maximum spring temperatures under each mid-century period using a GEV distribution yields a return period for an extreme temperature event with the daily maximum temperature at least 40.6°C in the city of Rio de Janeiro (analogous to the event on November 18, 2023 as measured by ERA5) of 51 years under SSP2-4.5 and 33 years under SSP5-8.5. By the end of the century under either emissions scenario, an event of this magnitude becomes much more likely, with return periods of 19 years or just 4 years under SSP2-4.5 and SSP5-8.5, respectively. Recent literature has suggested that the SSP5-8.5 scenario may not be realistic given our current socioeconomic, political, and physical landscape (Hausfather and Peters 2020 ; Burgess et al., 2020 ; Ritchie and Dowlatabadi 2017 ). However, these results indicate that an austral spring heatwave of the magnitude experienced in the city of Rio de Janeiro on November 18 is projected to become much more frequent in the future, even under the more stringent SSP2-4.5 emission pathway. We must also note that it is difficult to evaluate whether the models are missing emerging factors that could increase the frequency and intensity of these extreme heat events – such as Amazonian deforestation or declines in sea ice – reducing their ability to capture the possible future spring temperature distributions in Rio de Janeiro. Conclusions The November 2023 heatwave in the city of Rio de Janeiro was a record-breaking event characterized by meteorological conditions largely typical of spring extreme temperature events, but exceptional in their magnitudes. Rising temperatures were associated with positive geopotential height anomalies and corresponding atmospheric subsidence which facilitated clear sky conditions and increased sensible heat flux at the surface. These high pressure anomalies centered over the South Atlantic Subtropical High were likely related to the strong 2023-24 El Niño event. The subsidence near Rio de Janeiro associated with the geopotential height anomalies also suppressed precipitation and facilitated evaporation from the land surface, leading to decreased soil moisture and increased specific humidity. Moisture was available from multiple sources to facilitate these humidity increases, as Rio de Janeiro is a coastal city and downwind of both the Amazon and more local vegetation. SSTs off the coast of Rio de Janeiro were also highly elevated in the days before the heatwave peak, reducing the potential for coastal cooling. Finally, the event was terminated on November 19 due to the evaporative cooling, shading, and mixing associated with the onset of precipitation. The combination of changes in circulation, land surface feedbacks, and atmosphere-ocean interactions generated the conditions for an exceptionally intense and persistent extreme heat event in the city of Rio de Janeiro. The risk of extreme heat in austral spring is increasing significantly in Rio de Janeiro. We find that extreme spring temperature events are becoming more frequent throughout Brazil, and the extreme heat season is starting earlier and lasting longer than in previous decades in the city of Rio de Janeiro. Further, extreme heat of the magnitude on November 18, 2023 may become much more likely by mid- and end-of-century periods. However, the absolute increase in the frequency of similar heatwaves is largely dependent upon our future emissions pathway. The November 2023 heatwave had devastating impacts, including loss of life. As our climate continues to change and extreme heat in the city of Rio de Janeiro continues to increase in intensity and frequency, we can expect more strain on human health and cascading socioeconomic impacts. This extreme heat event was notable not only in its intensity, but also in its persistence. Consecutive extreme heat days have been shown to have nonlinear impacts on human health, in Brazil and in other countries, as they prevent individuals, buildings, and critical electrical equipment from cooling down between heat events (Geirinhas et al., 2020 ; Baldwin et al., 2019 ). More broadly, the direct impacts of heatwaves on hospitalizations throughout Brazil have been documented, with the largest effects occurring in long duration events (Zhao et al., 2019 ). These impacts of heatwaves on mortality are projected to increase, with particular consequences for elderly populations, especially if targeted adaptation measures are not put in place (Diniz et al., 2020 ). Continuing to improve our understanding of how and when extreme heat occurs is thus essential as our climate continues to change. This is particularly true for locations such as Rio de Janeiro, which historically has not been a hotspot of extreme heat – especially in the shoulder seasons – and thus individuals may not be well acclimated to extreme temperatures then (Periard et al., 2015; Horowitz 2016 ). The meteorological conditions surrounding the extreme heat event analyzed here demonstrate the potential for compound hazards throughout Brazil. The identified circulation pattern that establishes the atmospheric blocking associated with heatwaves in Rio de Janeiro is also likely linked to heavy precipitation events in South Brazil, an extreme case of which occurred in May 2024 in center-north of Rio Grande do Sul, including the metropolitan area of Porto Alegre, displacing hundreds of thousands and killing at least 155 people (Rogero 2024 ). The temporal compounding of these extreme temperature and flooding events within Brazil has the potential to strain the country’s disaster management systems more than events occurring in isolation. Furthermore, the exceptional spatial area within Brazil that experienced anomalous heat in the November 2023 event, relative to 99th percentile heat events in the city of Rio de Janeiro, underscores the potential for spatially compounding heat that could lead to outsized impacts. Exploring how unprecedented global surface ocean and surface temperatures, along with regional features like the broader heat and drought across much of Brazil, may contribute to extreme heat in the city of Rio de Janeiro will be an important component to improving our understanding of these compound events’ drivers, prediction capacity, and potential to change in the future. The evolving meteorological conditions associated with this heatwave were strongly impacted by the lack of precipitation in the first two weeks of November. This is particularly unexpected due to the fact that the active phase of the South American Monsoon System typically begins in late October or early November in this region (Marengo et al., 2012 ; Liebmann and Mechoso 2011; Raia and Cavalcanti 2008 ), which is linked to an increase in convective activity in tropical South America in the warm season (Jones and Carvalho 2013 ). Observational and modeling studies suggest that the South American Monsoon System dry season is lengthening (Arias et al., 2015 ; Fu et al., 2013 ) and that the onset of the active phase is delaying (Gomes et al., 2022 ; Pascale et al., 2019 ). These trends are projected to continue to some degree in the future with further climate change, particularly in light of ongoing deforestation which contributes to regional drying trends in the Amazon and other areas of Brazil (Boisier et al., 2015 ; Swann et al., 2015 ). Given Rio de Janeiro is a city with abundant access to moisture due to its proximity to the coast and vegetation, the increasingly constrained active monsoon phase could lead to increased frequency and intensity of extreme humid heat in the spring season (Ivanovich et al., 2024 ). Extensions of this work should be devoted to an exploration of these potential relationships. This work highlights the challenge of analyzing the drivers of weather extremes in such a climatically diverse city as Rio de Janeiro and emphasizes the need for future research to explore high resolution comparisons of mechanisms controlling the city’s microclimates. Differences between conditions recorded at individual weather stations within the city’s boundaries demonstrate the degree to which the dynamics of events in each neighborhood depend on the station’s location relative to the coast versus interior (Raymond and Mankin 2019 ), elevation (Raymond et al., 2022 ; Pepin et al., 2015 ), and degree of urbanization (Kruger et al., 2024; Chakraborty et al., 2022 ; Tan et al., 2010 ). Higher temporal resolution analysis would also better capture sub-daily processes such as sea breeze and their effect on extreme heat throughout the city. Further, many of these mechanisms influencing the intracity variability of heat stress exposure only focus on the effect of differences in dry bulb temperature. Factoring in the spatial variation in humidity, solar insolation, and windspeed complicate understanding, but are essential for capturing humans’ exposure to heat stress conditions. These intracity differences also meaningfully impact compound events with non-heat environmental hazards, such as floods, landslides, droughts, and air pollution, as well as how exposure to these hazards intersects with areas of social vulnerability. Future work should be devoted to investigating the different magnitudes of extreme heat and controlling mechanisms throughout Rio de Janeiro in order to inform targeted extreme heat adaptation plans for individual neighborhoods within the city. Declarations Correspondence to Catherine Ivanovich ( [email protected] ) Competing Interests: The authors declare that they have no conflict of interest. Author Contributions: S.J.C. conceived of the initial project concept. All co-authors contributed to study design, and C.I. performed the analysis. C.I. wrote the initial manuscript draft with the feedback and interpretation of all co-authors. All co-authors read and edited the manuscript. Acknowledgements: This work was partially supported and funded by Columbia Global at Columbia University, “Simulation of Extreme Weather Events in Brazilian Megacities”, a Climate Hub | Rio Project. Climate Hub | Rio is a knowledge, research, and innovation hub that brings together experts from Brazil, Columbia University, and around the world to advance climate-related knowledge and action in Rio and Brazil. Direct funding for C. Ivanovich and R. Horton was provided by National Oceanic and Atmospheric Administration’s Regional Integrated Sciences and Assessments program, Grant NA15OAR4310147. A. H. Sobel acknowledges support from NSF Grant AGS-1933523. S. J. Camargo is partially supported by the NOAA grant NA23OAR43201600. The authors declare no competing interests. Data Availability: The publicly available datasets used in this analysis are accessible via the following websites: HadISD, https://www.metoffice.gov.uk/hadobs/hadisd/ ; ERA5, https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview and https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview ; OISST, https://www.ncei.noaa.gov/products/optimum-interpolation-sst ; and NEXGDDP, https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6 . Station data from the Rio Alert System will be uploaded and accessible via a GitHub repository upon manuscript publication. Code Availability : All code used for the derivations, calculations, and data visualization will be made publicly available via a GitHub repository upon manuscript publication. References Alvares CA, Stape JL, Sentelhas PC, De Moraes Gonçalves JL (2013) Modeling monthly mean air temperature for Brazil. Theoret Appl Climatol 113(3–4):407–427. https://doi.org/10.1007/s00704-012-0796-6 Alvarez MS, Vera CS, Kiladis GN, Liebmann B (2016) Influence of the Madden Julian Oscillation on precipitation and surface air temperature in South America. Clim Dyn 46(1):245–262. https://doi.org/10.1007/s00382-015-2581-6 Arias PA, Fu R, Vera C, Rojas M (2015) A correlated shortening of the North and South American monsoon seasons in the past few decades. Clim Dyn 45(11):3183–3203. https://doi.org/10.1007/s00382-015-2533-1 Baldwin JW, Dessy JB, Vecchi GA, Oppenheimer M (2019) Temporally Compound Heat Wave Events and Global Warming: An Emerging Hazard. Earth’s Future 7(4):411–427. https://doi.org/10.1029/2018EF000989 Becker E, L’Heureux M, Hu Z-Z, Kumar A (2024) ENSO and the tropical Pacific. In State of the Climate in 2023. Bull Amer Meteor Soc 105(8):S221–S224. https://doi.org/10.1175/BAMS-D-24-0098.1 Bitencourt DP, Fuentes MV, Franke AE, Silveira RB, Alves MPA (2020) The climatology of cold and heat waves in Brazil from 1961 to 2016. Int J Climatol 40(4):2464–2478. https://doi.org/10.1002/joc.6345 Bitencourt DP, Alves M, Shibuya L, De Ângelo EK, Cunha D, I., De Souza E, J. P (2021) Climate change impacts on heat stress in Brazil—Past, present, and future implications for occupational heat exposure. Int J Climatol 41(S1). https://doi.org/10.1002/joc.6877 Boisier JP, Ciais P, Ducharne A, Guimberteau M (2015) Projected strengthening of Amazonian dry season by constrained climate model simulations. Nat Clim Change 5(7):656–660. https://doi.org/10.1038/nclimate2658 Burgess MG, Ritchie J, Shapland J, Pielke R (2020) IPCC baseline scenarios have over-projected CO2 emissions and economic growth. Environ Res Lett 16(1):014016. https://doi.org/10.1088/1748-9326/abcdd2 Cai W, McPhaden MJ, Grimm AM, Rodrigues RR, Taschetto AS, Garreaud RD et al (2020) Climate impacts of the El Niño–Southern Oscillation on South America. Nat Reviews Earth Environ 1(4):215–231. https://doi.org/10.1038/s43017-020-0040-3 Castelao RM, Barth JA (2006) Upwelling around Cabo Frio, Brazil: The importance of wind stress curl. Geophys Res Lett 33(3):2005GL025182. https://doi.org/10.1029/2005GL025182 Chakraborty T, Venter ZS, Qian Y, Lee X (2022) Lower Urban Humidity Moderates Outdoor Heat Stress. AGU Adv 3(5). https://doi.org/10.1029/2022AV000729 . e2022AV000729 Cheng Y-T, Lung S-CC, Hwang J-S (2019) New approach to identifying proper thresholds for a heat warning system using health risk increments. Environ Res 170:282–292. https://doi.org/10.1016/j.envres.2018.12.059 Cirino PH, Féres JG, Braga MJ, Reis E (2015) Assessing the Impacts of ENSO-related Weather Effects on the Brazilian Agriculture. Procedia Econ Finance 24:146–155. https://doi.org/10.1016/S2212-5671(15)00635-8 Coelho CAS, De Souza DC, Kubota PY, Cavalcanti IFA, Baker JCA, Figueroa SN et al (2022) Assessing the representation of South American monsoon features in Brazil and U.K. climate model simulations. Clim Resil Sustain 1(1):e27. https://doi.org/10.1002/cli2.27 Collins JM, Chaves RR, da Marques V S (2009) Temperature Variability over South America. J Clim 22(22):5854–5869. https://doi.org/10.1175/2009JCLI2551.1 Cordero Simões dos, Reis N, Boiaski NT, Ferraz SET (2019) Characterization and Spatial Coverage of Heat Waves in Subtropical Brazil. Atmosphere 10(5):284. https://doi.org/10.3390/atmos10050284 Correio Braziliense (2023), November 18 Rio bate recorde de calor do ano neste sábado (18/11) com 42,5°C. Retrieved May 14, 2024, from https://www.correiobraziliense.com.br/brasil/2023/11/6657444-rio-bate-recorde-de-calor-do-ano-neste-sabado-18-11-com-425-c.html Dereczynski C, Silva WL, Marengo J (2013) Detection and Projections of Climate Change in Rio de Janeiro, Brazil, 2013. https://doi.org/10.4236/ajcc.2013.21003 Diniz FR, Gonçalves FLT, Sheridan S (2020) Heat Wave and Elderly Mortality: Historical Analysis and Future Projection for Metropolitan Region of São Paulo. Brazil Atmos 11(9):933. https://doi.org/10.3390/atmos11090933 Dunn RJH (2019) HadISD version 3: monthly updates. Hadley Centre Technical Note Espinoza J-C, Jimenez JC, Marengo JA, Schongart J, Ronchail J, Lavado-Casimiro W, Ribeiro JVM (2024) The new record of drought and warmth in the Amazon in 2023 related to regional and global climatic features. Sci Rep 14(1):8107. https://doi.org/10.1038/s41598-024-58782-5 Feron S, Cordero RR, Damiani A, Llanillo PJ, Jorquera J, Sepulveda E et al (2019) Observations and Projections of Heat Waves in South America. Sci Rep 9(1):8173. https://doi.org/10.1038/s41598-019-44614-4 Fu R, Yin L, Li W, Arias PA, Dickinson RE, Huang L et al (2013) Increased dry-season length over southern Amazonia in recent decades and its implication for future climate projection. Proceedings of the National Academy of Sciences, 110(45), 18110–18115. https://doi.org/10.1073/pnas.1302584110 Geirinhas JL, Russo AC, Libonati R, Miralles DG, Sousa PM, Wouters H, Trigo RM (2022) The influence of soil dry-out on the record-breaking hot 2013/2014 summer in Southeast Brazil. Sci Rep 12(1):5836. https://doi.org/10.1038/s41598-022-09515-z Geirinhas JoãoL, Trigo RM, Libonati R, Coelho CAS, Palmeira AC (2018) Climatic and synoptic characterization of heat waves in Brazil. Int J Climatol 38(4):1760–1776. https://doi.org/10.1002/joc.5294 Geirinhas JoãoL, Trigo RM, Libonati R, Castro LCO, Sousa PM, Coelho CAS et al (2019) Characterizing the atmospheric conditions during the 2010 heatwave in Rio de Janeiro marked by excessive mortality rates. Sci Total Environ 650:796–808. https://doi.org/10.1016/j.scitotenv.2018.09.060 Geirinhas JoãoL, Russo A, Libonati R, Trigo RM, Castro LCO, Peres LF et al (2020) Heat-related mortality at the beginning of the twenty-first century in Rio de Janeiro, Brazil. Int J Biometeorol 64(8):1319–1332. https://doi.org/10.1007/s00484-020-01908-x Geirinhas JoãoL, Russo A, Libonati R, Sousa PM, Miralles DG, Trigo RM (2021) Recent increasing frequency of compound summer drought and heatwaves in Southeast Brazil. Environ Res Lett 16(3):034036. https://doi.org/10.1088/1748-9326/abe0eb Gomes GD, Nunes AMB, Libonati R, Ambrizzi T (2022) Projections of subcontinental changes in seasonal precipitation over the two major river basins in South America under an extreme climate scenario. Clim Dyn 58:1147–1169. https://doi.org/10.1007/s00382-021-05955-x Grimm AM (2019) Madden–Julian Oscillation impacts on South American summer monsoon season: precipitation anomalies, extreme events, teleconnections, and role in the MJO cycle. Clim Dyn 53(1):907–932. https://doi.org/10.1007/s00382-019-04622-6 Hausfather Z, Peters GP (2020) Emissions – the ‘business as usual’ story is misleading. Nature 577(7792):618–620. https://doi.org/10.1038/d41586-020-00177-3 He Z, Dai A, Vuille M (2021) The Joint Impacts of Atlantic and Pacific Multidecadal Variability on South American Precipitation and Temperature. J Clim 34(19):7959–7981. https://doi.org/10.1175/JCLI-D-21-0081.1 Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz-Sabater J et al (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146(730):1999–2049. https://doi.org/10.1002/qj.3803 Horowitz M (2016) Epigenetics and cytoprotection with heat acclimation. Journal of Applied Physiology (Bethesda, Md.: 1985), 120(6), 702–710. https://doi.org/10.1152/japplphysiol.00552.2015 Huang B, Liu C, Banzon V, Freeman E, Graham G, Hankins B et al (2021) Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1. J Clim 34(8):2923–2939. https://doi.org/10.1175/JCLI-D-20-0166.1 Hughs E, Jeantet D (2023), November 15 It’s not yet summer in Brazil, but a dangerous heat wave is sweeping the country. Retrieved May 14, 2024, from https://apnews.com/article/brazil-heat-wave-climate-environment-wildfires-1e4714fb2c6566120c13cf4e2b657f7d Instituto Brasileiro de Geografia e Estatística (2022) Demographic Census 2022. Retrieved from https://sidra.ibge.gov.br/pesquisa/censo-demografico/demografico-2022/primeiros-resultados-populacao-e-domicilios Ivanovich CC, Horton RM, Sobel AH, Singh D (2024) Subseasonal Variability of Humid Heat During the South Asian Summer Monsoon. Geophysical Research Letters, 51(6), e2023GL107382. https://doi.org/10.1029/2023GL107382 Jones C, Carvalho LMV (2013) Climate change in the South American Monsoon System: Present climate and CMIP5 projections. J Clim 26(17):6660–6678. https://doi.org/10.1175/JCLI-D-12-00412.1 Jornal Nacional (2023), November 18 Taylor Swift: segundo show é adiado, por causa do calor extremo no Rio. Retrieved April 16, 2024, from https://g1.globo.com/jornal-nacional/noticia/2023/11/18/taylor-swift-show-e-adiado-apos-morte-de-fa.ghtml Krüger E, Gobo JPA, Tejas GT, da, Silva de Souza RM, Neto JBF, Pereira G et al (2024) The impact of urbanization on heat stress in Brazil: A multi-city study. Urban Climate, 53, 101827. https://doi.org/10.1016/j.uclim.2024.101827 Lanzante JR, Dixon KW, Nath MJ, Whitlock CE, Adams-Smith D (2018) Some Pitfalls in Statistical Downscaling of Future Climate. Bull Am Meteorol Soc 99(4):791–803. https://doi.org/10.1175/BAMS-D-17-0046.1 Latinović D, Chou SC, Rančić M, Medeiros GS, Lyra ADA (2019) Seasonal climate and the onset of the rainy season in western-central Brazil simulated by Global Eta Framework model. Int J Climatol 39(3):1429–1445. https://doi.org/10.1002/joc.5892 Lemus-Canovas M, Insua-Costa D, Trigo RM, Miralles DG (2024) Record-shattering 2023 Spring heatwave in western Mediterranean amplified by long-term drought. Npj Clim Atmospheric Sci 7(1):1–8. https://doi.org/10.1038/s41612-024-00569-6 Libonati R, Geirinhas JL, Silva PS, Dos Santos M, Rodrigues D, Russo JA, A., et al (2022) Drought–heatwave nexus in Brazil and related impacts on health and fires: A comprehensive review. Ann N Y Acad Sci 1517(1):44–62. https://doi.org/10.1111/nyas.14887 Liebmann B, Mechoso CR, In C-P, Chang Y, Ding N-C, Lau RH, Johnson B, Wang, Yasunari T (2011) World Scientific Series on Asia-Pacific Weather and Climate (2nd edn., vol 5, 137–157). WORLD SCIENTIFIC. https://doi.org/10.1142/9789814343411_0009 Lyra GB, Correia TP, de Oliveira-Júnior JF, Zeri M (2018) Theoret Appl Climatol 134(3):955–965. https://doi.org/10.1007/s00704-017-2322-3 . Evaluation of methods of spatial interpolation for monthly rainfall data over the state of Rio de Janeiro, Brazil Marengo JA, Liebmann B, Grimm AM, Misra V, Silva Dias PL, Cavalcanti IFA et al (2012) Recent developments on the South American monsoon system. Int J Climatol 32(1):1–21. https://doi.org/10.1002/joc.2254 Marengo JA, Soares WR, Saulo C, Nicolini M (2004) Climatology of the Low-Level Jet East of the Andes as Derived from the NCEP–NCAR Reanalyses: Characteristics and Temporal Variability. J Clim 17(12):2261–2280. https://doi.org/10.1175/1520-0442(2004)0172.0.CO;2 Montini TL, Jones C, Carvalho LMV (2019) The South American Low-Level Jet: A New Climatology, Variability, and Changes. J Geophys Research: Atmos 124(3):1200–1218. https://doi.org/10.1029/2018JD029634 Mora C, Dousset B, Caldwell IR, Powell FE, Geronimo RC, Bielecki CR et al (2017) Global risk of deadly heat. Nat Clim Change 7(7):501–506. https://doi.org/10.1038/nclimate3322 Nahlik MJ, Chester MV, Pincetl SS, Eisenman D, Sivaraman D, English P (2017) Building Thermal Performance, Extreme Heat, and Climate Change. J Infrastruct Syst 23(3):04016043. https://doi.org/10.1061/(asce)is.1943-555x.0000349 El Niño 2023 | El Niño/La Niña Watch & NASA JPL, Retrieved PDO (2024), January 23 May 14, 2024, from https://sealevel.jpl.nasa.gov/data/el-nino-la-nina-watch-and-pdo/el-nino-2023 Nguyen B (2023), December 27 Brazilian Taylor Swift Fan Died Of Heat Exhaustion At Rio Concert. Retrieved April 16, 2024, from https://www.forbes.com/sites/britneynguyen/2023/12/27/brazilian-taylor-swift-fan-died-of-heat-exhaustion-at-rio-concert/ Palma ED, Matano RP (2009) Disentangling the upwelling mechanisms of the South Brazil Bight. Cont Shelf Res 29(11):1525–1534. https://doi.org/10.1016/j.csr.2009.04.002 Pascale S, Carvalho LMV, Adams DK, Castro CL, Cavalcanti IFA (2019) Current and Future Variations of the Monsoons of the Americas in a Warming Climate. Curr Clim Change Rep 5(3):125–144. https://doi.org/10.1007/s40641-019-00135-w Pepin N, Bradley RS, Diaz HF, Baraer M, Caceres EB, Forsythe N et al (2015) Elevation-dependent warming in mountain regions of the world. Nat Clim Change 5(5):424–430. https://doi.org/10.1038/nclimate2563 Périard JD, Racinais S, Sawka MN (2015) Adaptations and mechanisms of human heat acclimation: Applications for competitive athletes and sports. Scand J Med Sci Sports 25(Suppl 1):20–38. https://doi.org/10.1111/sms.12408 Powis CM, Byrne D, Zobel Z, Gassert KN, Lute AC, Schwalm CR (2023) Observational and model evidence together support wide-spread exposure to noncompensable heat under continued global warming. Sci Adv 9(36):eadg9297. https://doi.org/10.1126/sciadv.adg9297 Perkins SE, Pitman AJ, Holbrook NJ, McAneney J (2007) Evaluation of the AR4 Climate Models’ Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions. J Clim 20(17):4356–4376. https://doi.org/10.1175/JCLI4253.1 Raia A, Cavalcanti IFA (2008) The Life Cycle of the South American Monsoon System. J Clim 21(23):6227–6246. https://doi.org/10.1175/2008JCLI2249.1 Raymond C, Mankin JS (2019) Assessing present and future coastal moderation of extreme heat in the Eastern United States. Environ Res Lett 14(11):114002. https://doi.org/10.1088/1748-9326/ab495d Raymond C, Matthews T, Horton RM (2020) The emergence of heat and humidity too severe for human tolerance. Sci Adv 6(19):eaaw1838. https://doi.org/10.1126/sciadv.aaw1838 Raymond C, Waliser D, Guan B, Lee H, Loikith P, Massoud E et al (2022) Regional and Elevational Patterns of Extreme Heat Stress Change in the US. https://doi.org/10.1088/1748-9326/ac7343 . Environmental Research Letters Regoto P, Dereczynski C, Chou SC, Bazzanela AC (2021) Observed changes in air temperature and precipitation extremes over Brazil. Int J Climatol 41(11):5125–5142. https://doi.org/10.1002/joc.7119 Rehbein A, Ambrizzi T (2023) ENSO teleconnections pathways in South America. Clim Dyn 61(3):1277–1292. https://doi.org/10.1007/s00382-022-06624-3 Ritchie J, Dowlatabadi H (2017) Why do climate change scenarios return to coal? Energy. 140:1276–1291. https://doi.org/10.1016/j.energy.2017.08.083 Rogero T (2024), May 19 Brazil counts cost of worst-ever floods with little hope of waters receding soon. The Guardian. Retrieved from https://www.theguardian.com/world/article/2024/may/19/brazil-floods-toll Rogers CDW, Ting M, Li C, Kornhuber K, Coffel ED, Horton RM et al (2021) Recent Increases in Exposure to Extreme Humid-Heat Events Disproportionately Affect Populated Regions. Geophys Res Lett 48(19). https://doi.org/10.1029/2021GL094183 Shimizu MH, Ambrizzi T (2016) MJO influence on ENSO effects in precipitation and temperature over South America. Theoret Appl Climatol 124(1):291–301. https://doi.org/10.1007/s00704-015-1421-2 Shreevastava A, Raymond C, Hulley GC (2023) Contrasting Intraurban Signatures of Humid and Dry Heatwaves over Southern California. J Appl Meteorol Climatology 62(6):709–720. https://doi.org/10.1175/JAMC-D-22-0149.1 Sistema Alerta Rio da Prefeitura do Rio de Janeiro (2024) Meteorological Data. Retrieved from https://alertario.rio.rj.gov.br/download/dados-meteorologicos/ de Sistema IBGE (2024) Recuperação Automática - SIDRA. (n.d.). Retrieved April 21, from https://sidra.ibge.gov.br/pesquisa/censo-demografico/demografico-2022/primeiros-resultados-populacao-e-domicilios Son J-Y, Gouveia N, Bravo MA, de Freitas CU, Bell ML (2016) The impact of temperature on mortality in a subtropical city: effects of cold, heat, and heat waves in São Paulo, Brazil. Int J Biometeorol 60(1):113–121. https://doi.org/10.1007/s00484-015-1009-7 Swann ALS, Longo M, Knox RG, Lee E, Moorcroft PR (2015) Future deforestation in the Amazon and consequences for South American climate. Agric For Meteorol 214–215. https://doi.org/10.1016/j.agrformet.2015.07.006 Tan J, Zheng Y, Tang X, Guo C, Li L, Song G et al (2010) The urban heat island and its impact on heat waves and human health in Shanghai. Int J Biometeorol 54(1):75–84. https://doi.org/10.1007/s00484-009-0256-x Thrasher B, Wang W, Michaelis A, Melton F, Lee T, Nemani R (2022) Sci Data 9(1):262. https://doi.org/10.1038/s41597-022-01393-4 . NASA Global Daily Downscaled Projections, CMIP6 United Nations Department of Economic and Social Affairs Population Division (2022) Population of Urban Agglomerations with 300,000 Inhabitants or More in 2018, by country, 1950–2035 (thousands). Retrieved from https://population.un.org/wup/Download/ US EPA O (2021), February 4 Climate Change Indicators: Heat Waves [Reports and Assessments]. Retrieved April 23, 2024, from https://www.epa.gov/climate-indicators/climate-change-indicators-heat-waves Vecellio DJ, Wolf ST, Cottle RM, Kenney WL (2022) Evaluating the 35°C wet-bulb temperature adaptability threshold for young, healthy subjects (PSU HEAT Project). J Appl Physiol 132(2):340–345. https://doi.org/10.1152/japplphysiol.00738.2021 Vera C, Baez J, Douglas M, Emmanuel CB, Marengo J, Meitin J et al (2006) The South American Low-Level Jet Experiment. Bull Am Meteorol Soc 87(1):63–78. https://doi.org/10.1175/BAMS-87-1-63 Wilby RL, Kasei R, Gough KV, Amankwaa EF, Abarike M, Anderson NJ et al (2021) Monitoring and moderating extreme indoor temperatures in low-income urban communities. Environ Res Lett 16(2):024033. https://doi.org/10.1088/1748-9326/abdbf2 You Y, Ting M, Biasutti M (2024) Climate warming contributes to the record-shattering 2022 Pakistan rainfall. Npj Clim Atmospheric Sci 7(1):1–8. https://doi.org/10.1038/s41612-024-00630-4 Zhao Q, Li S, Coelho MSZS, Saldiva PHN, Hu K, Huxley RR et al (2019) The association between heatwaves and risk of hospitalization in Brazil: A nationwide time series study between 2000 and 2015. PLoS Med 16(2):e1002753. https://doi.org/10.1371/journal.pmed.1002753 Additional Declarations The authors declare no competing interests. Supplementary Files Ivanovich2025WCDSupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5355924","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439638861,"identity":"505e65ea-b728-4851-ac7c-40e3741fb965","order_by":0,"name":"Catherine C. Ivanovich","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYFACxsYHCQb/5RgYmBsYGBskQEIGhLQ0GzyoYDYGMojWwsAm+eAMc2IDRAsDYS387YcbJBLb2NL72xvbpG7usJBnYG/eJoFPi8SZxAaDxDae3BlnDrZJ556RMGzgOVaGV4uBBGNDQmKbRO4GoF3SuW1ArkSOGUEtBxLbDNINoFrsG+TfENTS2JBwJiEBpiWxQYIHvxagX5oZEioOGAL90mwN9EtyG09asQU+Lfztx5///GFwQJ6/vfng7dwddbb97Ic33sCnBROwkaZ8FIyCUTAKRgE2AAB0UEmdjgdzCgAAAABJRU5ErkJggg==","orcid":"","institution":"Columbia University","correspondingAuthor":true,"prefix":"","firstName":"Catherine","middleName":"C.","lastName":"Ivanovich","suffix":""},{"id":439638862,"identity":"b767027c-91f3-4403-af85-8ac50eccb3f3","order_by":1,"name":"Adam H. Sobel","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Adam","middleName":"H.","lastName":"Sobel","suffix":""},{"id":439638863,"identity":"d164b86f-6b2c-4eb8-a0d3-32dcc270e296","order_by":2,"name":"Radley M. Horton","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Radley","middleName":"M.","lastName":"Horton","suffix":""},{"id":439638864,"identity":"e0022df7-f3d0-4b21-9d2c-cbd76f437fa6","order_by":3,"name":"Ana M. B. Nunes","email":"","orcid":"","institution":"Universidade Federal do Rio de Janeiro","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"M. B.","lastName":"Nunes","suffix":""},{"id":439638865,"identity":"b9b800a6-0dbf-47f9-a6de-6afdd7c70235","order_by":4,"name":"Rosmeri Porfírio Rocha","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Rosmeri","middleName":"Porfírio","lastName":"Rocha","suffix":""},{"id":439638866,"identity":"6d679b12-91a6-4f7c-80b8-9191b84bc440","order_by":5,"name":"Suzana J. Camargo","email":"","orcid":"","institution":"Columbia University","correspondingAuthor":false,"prefix":"","firstName":"Suzana","middleName":"J.","lastName":"Camargo","suffix":""}],"badges":[],"createdAt":"2024-10-29 16:23:15","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5355924/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-5355924/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80419769,"identity":"5a328469-9c4c-473a-8e1d-1e08cdf2f3b3","added_by":"auto","created_at":"2025-04-11 18:34:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":166066,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical climatology and 2023 recorded a) daily maximum temperature, b) daily mean specific humidity, and c) daily maximum wet bulb temperature in the city of Rio de Janeiro. Data from the Galeão International Airport weather station as reported by the HadISD dataset. Vertical dashed line identifies record-breaking temperature event on November 18, 2023.\u003c/p\u003e","description":"","filename":"f1.png","url":"https://assets-eu.researchsquare.com/files/rs-5355924/v2/6c96bab0fd9f3375c10a6b78.png"},{"id":80419767,"identity":"939f4cc5-b571-489c-b2ff-ed863b6c23a1","added_by":"auto","created_at":"2025-04-11 18:34:28","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74773,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial maps of daily maximum temperatures during the date of peak extreme heat intensity in the city of Rio de Janeiro using ERA5 data (shading) and the Galeão International Airport weather station (marker). Vectors represent surface winds; contours represent elevation in meters. A) Climatology during November 18 throughout the historical record. B) Magnitudes on November 18, 2023. C) Anomalies during November 18, 2023.\u003c/p\u003e","description":"","filename":"f2.png","url":"https://assets-eu.researchsquare.com/files/rs-5355924/v2/49a05625afa3947f90963c5e.png"},{"id":80420444,"identity":"6de66a68-8158-46dd-96d0-d800c931e79b","added_by":"auto","created_at":"2025-04-11 18:42:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":392158,"visible":true,"origin":"","legend":"\u003cp\u003eMagnitude (left) and anomalies (right) of daily maximum temperature, mean soil moisture, mean specific humidity, total precipitation, and mean SST on day of peak temperature in the city of Rio de Janeiro (November 18, 2023). Overlying wind vectors and 500 hPa geopotential height contours (50 m and 25 m contour levels for magnitude and anomaly plots, respectively). Anomalies calculated relative to historical calendar date mean values across the period from 1979-2023. Inset in the upper right corner of each anomaly plot zooms in on the white box surrounding the city of Rio de Janeiro (purple marker) in the top right subplot.\u003c/p\u003e","description":"","filename":"f3.png","url":"https://assets-eu.researchsquare.com/files/rs-5355924/v2/33671f1a6292ab0468b45517.png"},{"id":80419772,"identity":"e3ffee41-222b-4bc3-ba12-761a86dd8aa9","added_by":"auto","created_at":"2025-04-11 18:34:29","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":355328,"visible":true,"origin":"","legend":"\u003cp\u003eAnalogous plot to Fig. 3, but composited on 99\u003csup\u003eth\u003c/sup\u003e percentile extreme temperature days in the September-November (SON) season for the ERA5 grid cell which includes the Galeão International Airport weather station.\u003c/p\u003e","description":"","filename":"f4.png","url":"https://assets-eu.researchsquare.com/files/rs-5355924/v2/51371f755f50efa44bdb5f7b.png"},{"id":80420647,"identity":"e9c14c7c-00dc-4d13-a08b-1f5b7de3c277","added_by":"auto","created_at":"2025-04-11 18:50:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":261338,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of meteorological conditions during the month of November 2023 in Rio de Janeiro. Grey lines in the background of each subplot show the evolution of all variables, with individual variables compared in colors to dry bulb temperature in red. Vertical dashed line identifies record-breaking temperature event on November 18, 2023. All variables are calculated for the grid cell which includes the Galeão International Airport weather station except SST, which is averaged over the box 21°S-24°S and 42°W-45°E.\u003c/p\u003e","description":"","filename":"f5.png","url":"https://assets-eu.researchsquare.com/files/rs-5355924/v2/ccbd3d44fb2459d07aae3b5f.png"},{"id":80419777,"identity":"d97fd923-4005-4099-8020-a9c6d4f1a821","added_by":"auto","created_at":"2025-04-11 18:34:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":300886,"visible":true,"origin":"","legend":"\u003cp\u003eEvolution of the geopotential height at 200 hPa (contours) in the weeks of suppressed precipitation leading up to the extreme heat event on November 18 in the city of Rio de Janeiro. Geopotential height anomaly contour levels are at 100 m, with positive (negative) anomalies in solid (dashed) contours. Across all subplots, shading indicates November 2023 mean SST anomalies.\u003c/p\u003e","description":"","filename":"f6.png","url":"https://assets-eu.researchsquare.com/files/rs-5355924/v2/ff8f642026e112b83ead5529.png"},{"id":80419784,"identity":"a6ce2c3c-3f3d-4b8f-bfb1-2deab27d108b","added_by":"auto","created_at":"2025-04-11 18:34:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":81947,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical trend from 1979-2023 in the number of days per year above a) 30°C and b) locally defined 90\u003csup\u003eth\u003c/sup\u003e percentile. Stippling shows areas which are not significant at a p = 0.05 level. c) Trend in number of days per year above 30°C taking place in the SON season in ERA5 for the grid cell which includes the Galeão International Airport weather station.\u003c/p\u003e","description":"","filename":"f7.png","url":"https://assets-eu.researchsquare.com/files/rs-5355924/v2/6034a1ec32102687a5270134.png"},{"id":80420449,"identity":"7ded73cd-ce1a-468d-a810-ee117ae76935","added_by":"auto","created_at":"2025-04-11 18:42:29","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":96262,"visible":true,"origin":"","legend":"\u003cp\u003eShifting timing of the city of Rio de Janeiro extreme heat season. Horizontal axis indicates the year in which winter begins (“January” marking denotes the start of the following calendar year). Colored markers indicate the first and last days of the extreme heat season each year. Marker color indicates whether the start/end date is lengthening (red) or shortening (blue) the heat season compared to historical mean start/end dates. Dashes indicate individual additional days with daily maximum temperatures surpassing 31.4°C (no persistence required). Grey shading indicates area between trend lines in the shifting seasonality.\u003c/p\u003e","description":"","filename":"f8.png","url":"https://assets-eu.researchsquare.com/files/rs-5355924/v2/f85cf467960036bd784d1c2b.png"},{"id":80420448,"identity":"4b7197d5-cf30-4011-9e19-97e5aa5729de","added_by":"auto","created_at":"2025-04-11 18:42:29","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":70372,"visible":true,"origin":"","legend":"\u003cp\u003eGeneralized Extreme Value distributions for SON maximum temperatures during early and late historical periods (observed in ERA5), mid-century periods, and end-of-century periods under SSP2-4.5 and SSP5-8.5 (projections from bias-corrected NEXGDDP data). Diamonds indicate the value of the location parameter for each distribution. Vertical grey line shows the magnitude of the extreme temperature event on November 18, 2023.\u003c/p\u003e","description":"","filename":"f9.png","url":"https://assets-eu.researchsquare.com/files/rs-5355924/v2/704db896a1f7810f3017b9e7.png"},{"id":80421173,"identity":"94d7ff90-37ed-4637-abfb-209d3f9b36de","added_by":"auto","created_at":"2025-04-11 18:58:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2303339,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5355924/v2/a07d5ac4-cc8c-4964-a536-3fbefedd5b4e.pdf"},{"id":80420454,"identity":"1948bb4a-e4d5-4699-8bc8-b4091dbe2df0","added_by":"auto","created_at":"2025-04-11 18:42:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3167375,"visible":true,"origin":"","legend":"","description":"","filename":"Ivanovich2025WCDSupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5355924/v2/f96a6c7df50aea99296eafd5.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"Physical Drivers of the November 2023 Heatwave in Rio de Janeiro","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the spring of 2023, the city of Rio de Janeiro experienced a high impact heatwave that caught the world\u0026rsquo;s attention. Media sources ranging from local reporting to international news companies centered stories on the event\u0026rsquo;s record-breaking temperature magnitudes and unseasonal timing, arriving earlier in the warm season than typical heatwaves (Correio Braziliense, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Hughs and Jeanet, 2023). The impacts of the extreme heat were widely publicized in part due to the tragic death of a concertgoer hospitalized during a Taylor Swift performance in Rio de Janeiro on November 17, with news articles reporting heat-induced cardiovascular distress as the cause of death (Nguyen, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Sources also report that the stadium in which the concert took place experienced higher temperatures than those measured in the open air, as well as a lack of cooling equipment and insufficient water for attendees (Nguyen, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jornal Nacional, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Such complexities highlight that extreme heat experienced by individuals on the ground can far exceed temperatures measured at local weather stations, depending on infrastructure and the capacity for cooling interventions (Wilby et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Nahlik et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, the meteorological event itself is of course one of the preconditions for societal impacts. We therefore explore the physical mechanisms behind the heatwave as one key step towards improving preparation for the impact of future extreme heat events.\u003c/p\u003e\u003cp\u003eThroughout Brazil, the highest temperatures occur climatologically in low latitude and low altitude regions in the interior of the country, such as the cities of Teresina (Piau\u0026iacute;, in the Northeast region of Brazil) and Palmas (Tocantins, in the Central-West region of Brazil; Alvares et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), both of which are far from Rio de Janeiro. Extreme temperatures tend to be intensified by land-atmosphere interactions, as dry soils partition more energy into sensible heat (Geirinhas et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These relationships between the atmosphere and land surface processes increase the likelihood of compound extreme heat and drought events and intensify impacts on agriculture (Cirino et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), worker productivity for outdoor laborers (Bitencourt et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), wildfire risk (Libonati et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and direct impacts on human health (Zhao et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). As the frequency and intensity of extreme heat throughout Brazil has increased significantly in the past decades and is projected to continue in the future (Feron et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Regoto et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bitencourt et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the widespread socio-economic impacts of these events are likely to grow.\u003c/p\u003e\u003cp\u003eWhile Rio de Janeiro is the second most populous city in Brazil (Instituto Brasileiro de Geografia e Estat\u0026iacute;stica \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and the third most populous city in South America (United Nations Department of Economic and Social Affairs Population Division \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), few studies have focused on extreme heat in the city. On one hand, Rio de Janeiro has not historically been a major hotspot of extreme heat in Brazil and has experienced fewer heatwaves relative to other major cities in the country (Geirinhas et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Further, the numerous microclimates within the city, influenced by its coastal setting and complex topography, complicate the study of local heatwave dynamics. Indeed, there is large spatial variability in temperature extremes across the Rio de Janeiro metropolitan area compared to other Brazilian cities (Alvares et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). However, impactful heatwaves in recent decades have increasingly drawn attention from public health officials and scientific communities alike. Recent literature has explored the dynamics and mortality impacts of extreme temperatures during heatwaves in 2010 (Geirinhas et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and 2013/2014 (Geirinhas et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and work has begun to investigate compound heatwave and drought events throughout Southeast Brazil (Geirinhas et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). There is also building evidence that temperature extremes are increasing in intensity and frequency throughout Brazil, including the city of Rio de Janeiro (Regoto et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Bitencourt et al., 2019). Climate variability also plays an important role in modulating temperatures over this area, including large scale modes of climate variability such as the El Ni\u0026ntilde;o-Southern Oscillation (Rehbein and Ambrizzi \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Cai et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shimizu and Ambrizzi 2015), the Pacific Decadal Oscillation, and the Atlantic Multidecadal Oscillation (He et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Should more intense, frequent, and unseasonably early extreme heat events take place in the future in the city of Rio de Janeiro, these heatwaves may have increased impacts on human health due to potential exceedance of unprecedented temperature thresholds and individuals\u0026rsquo; lack of preparation for these events. In a tropical city where baseline temperatures are already relatively high, small shifts in the temperature distribution can have large impacts on the frequency of extremes (Cheng et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), particularly at thresholds relevant to human health outcomes (Vecellio et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These health risks are compounded by the humidity in Rio de Janeiro, a coastal city with ample moisture sources from the ocean and surrounding vegetation, priming the region for humid heat extremes which are physiologically more dangerous to human health than dry heat (Mora et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn this study, we explore the meteorological conditions that led to the extreme heat event in November 2023 in the city of Rio de Janeiro. We identify drivers of the exceptional magnitude and persistence of the extreme temperatures, as well as their early arrival in the calendar year. We compare these conditions to those associated with typical heatwaves in the region, and particularly events taking place in the spring season. We then consider how extreme spring temperature events have shifted throughout the historical period, and how we might expect them to change in the future with ongoing anthropogenic climate change.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data\u003c/h2\u003e \u003cp\u003eThis analysis employs both station-based observations and reanalysis data. Initial analyses are conducted on subdaily station data from the city of Rio de Janeiro, accessed via the Met Office Hadley Center\u0026rsquo;s HadISD station-based dataset (Dunn \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and the Rio Alert System produced by the Rio de Janeiro City Hall (Sistema Alerta Rio da Prefeitura do Rio de Janeiro \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Three airport weather stations are available from HadISD for the city of Rio de Janeiro, namely the Gale\u0026atilde;o/Antonio Carlos Jobim International Airport (located on the island Ilha do Governador within the Guanabara Bay), the Campo D\u0026eacute;lio Jardim De Mattos Airport (an Air Force base located in the city\u0026rsquo;s North Zone), and the Santos Dumont Airport (a waterfront airport located near the city center). Six additional stations from the Rio Alert System dataset record measurements from the top of various community and commercial buildings, including hotels, schools, and warehouses. These stations are located in distinct areas of the city, whose topographical and coastal complexities contribute to various microclimates. Weather and climate recorded by each of these stations is thus slightly distinct (see Fig. S1), which is a challenge that has been previously identified in the literature (Lyra et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Dereczynski et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). We therefore base the majority of our analysis on reanalysis data and compare the identified patterns with station data when possible. This comparison is particularly important for extreme events, as the magnitude of extreme heat has been shown to be biased in reanalysis products due to their spatial and temporal smoothing of observations (Rogers et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Raymond et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Further, the human experience of heat stress is inherently hyperlocal, meaning that the distinct microclimates existing throughout the city can control heat stress exposure and the efficiency of adaptation strategies. However, the present study is primarily concerned with the regional drivers of the extreme event rather than its absolute magnitude. Reanalysis provides continuous spatial coverage and a wide array of internally consistent meteorological variables, which warrants its use for the application here.\u003c/p\u003e \u003cp\u003eHourly meteorological data are retrieved from the fifth major global reanalysis of the European Centre for Medium-Range Weather Forecasts (ERA5), including 2-meter temperature, 2-meter dewpoint temperature, volumetric soil water for layer 1 (0\u0026ndash;7 cm, where the surface is at 0 cm), surface pressure, geopotential height at 500 hPa and 200 hPa, precipitation, evaporation, 2-meter horizontal winds, and vertical velocity at 500 hPa (Hersbach et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). From this hourly data, daily maximum temperature, daily total precipitation, and daily means of all other variables are calculated from 1979\u0026ndash;2023. Daily mean sea surface temperature (SST) data from 1979\u0026ndash;2023 is also retrieved from the NOAA 1/4\u0026deg; Daily Optimum Interpolation Sea Surface Temperature (OISST) dataset (Huang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe also explore the future evolution of temperature extremes over the city of Rio de Janeiro using the NEXGDDP dataset (Thrasher et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This data product is statistically downscaled from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models, with a spatial resolution of 0.25 degrees and outputs variables on a daily temporal scale. We directly retrieve daily maximum temperature data through the end of the century under the Shared Socioeconomic Pathways (SSPs) SSP2-4.5 and SSP5-8.5 for the 23 models which output this variable and pair of scenarios for each day in the calendar year through 2100.\u003c/p\u003e \u003cp\u003eBecause of Rio de Janeiro\u0026rsquo;s complex coastal and mountainous terrain, projections data may not accurately capture fine scale differences in the city\u0026rsquo;s climate. For example, recent literature has shown that the coastal cooling relative to inland areas experienced in regions such as the eastern United States may be underestimated by models (Raymond and Mankin \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, models are particularly biased in regions with large land-ocean surface temperature contrasts, and Rio de Janeiro\u0026rsquo;s location as a tropical city and the fact that the extreme events analyzed in this study take place in the spring when this temperature gradient should be relatively small suggest that these biases may be muted compared to other regions and seasons. In order to address these potential sources of error, we generate a set of synthetic time series based on NEXGDDP projections which retain the seasonality and variability recorded in the historical reanalysis data from ERA5. We use a percentile matching technique in which we first bin all data for the grid cell which includes the city of Rio de Janeiro during a historical base period (1981\u0026ndash;2013) into one-percentile bins for both the NEXGDDP and ERA5 datasets. We additionally bin all NEXGDDP data from this gridcell into one-percentile bins during one midcentury period (2041\u0026ndash;2060) and one end-of-century period (2081\u0026ndash;2100). We then calculate the temperature delta for each percentile bin between the base period and both the midcentury and the end-of-century periods in the NEXGDDP data. Finally, we apply these percentile specific change factors to each associated bin in the historical ERA5 base period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Methodology\u003c/h2\u003e \u003cp\u003eWe first create time series for the historical day-of-year climatologies of variables in the city of Rio de Janeiro and compare them to the evolution throughout 2023. All anomalies are calculated relative to historical mean calendar date values (i.e., the daily maximum temperature anomaly on November 18, 2023 is calculated by subtracting the mean daily maximum temperatures on November 18 in all previous years in the historical record from the recorded absolute magnitude of the event). We also generate maps of concurrent meteorological variables relevant to the extreme heat event for the greater region outside of Rio de Janeiro. We compare these spatial patterns to those experienced during previous extreme heat events in Rio de Janeiro, calculated as 99th percentile daily maximum temperature days across all seasons for the grid cell which includes the Gale\u0026atilde;o International Airport weather station. We then select for events which only occur in the September-November (SON) austral spring season.\u003c/p\u003e \u003cp\u003eWe also quantify how extreme heat in the city of Rio de Janeiro is shifting using a variety of methods. We first consider how the frequency of extreme temperatures is changing over time in Brazil and define these extreme temperatures using both absolute and relative thresholds. We select these thresholds as 30\u0026deg;C and the locally defined 90th percentile daily maximum temperature at each grid cell. These thresholds are chosen in order to investigate impactful temperature magnitudes while ensuring sufficient sample size for the trend analysis.\u003c/p\u003e \u003cp\u003eWe also visualize the broadening of the extreme heat season, calculated based on the number of days between the start of the first heatwave and end of the last heatwave of the season. A heatwave is defined here as a three-day period with consecutive daily maximum temperatures above the 50th percentile of daily maximum temperatures across the two hottest months of the year in the city of Rio de Janeiro (January and February); this 50th percentile threshold is equal to about 31.4\u0026deg;C. This heat season definition is informed by a definition used by the United States Environmental Protection Agency (US EPA \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), adapted to better reflect Rio de Janeiro\u0026rsquo;s lower temporal variability in temperature due to its tropical location.\u003c/p\u003e \u003cp\u003eFinally, we calculate how spring temperature distributions have already changed in Rio de Janeiro by comparing early and late historical periods in ERA5 for the grid cell which includes the Gale\u0026atilde;o International Airport weather station. Distributions are calculated from annual spring maximum temperatures in the city of Rio de Janeiro and fit using GEV distributions, which have been shown to well capture extreme temperature distributions (Powis et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Van Oldenborgh et al., 2022). For comparison, we also plot GEV distributions for early and late historical periods in the NEXGDDP model data before applying our bias-correction technique. The location parameter and spread of the model data distributions during these periods is much lower than that of ERA5 (Fig. S2), further motivating our use of synthetic time series to explore how these distributions may change in the future.\u003c/p\u003e \u003cp\u003eWe then use the bias-corrected NEXGDDP data for the 23 models which report daily maximum temperature for each day in the calendar year under the aforementioned SSP2-4.5 and SSP5-8.5 scenarios during a midcentury and end-of-century time period. We additionally evaluate the impact of only using models which most accurately reproduce the historical observed daily maximum temperature record in the city of Rio de Janeiro. We calculate the Perkins skill score to evaluate the similarity between probability density functions of daily maximum temperature in the reanalysis dataset (ERA5) and each of the 23 global climate models during the historical period. These skill scores are calculated as the cumulative minimum between the observed and modeled distributions of each binned value (Perkins et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). We finally select the 6 climate models which exhibit skill scores greater than 0.8, indicating that these models capture over 80% of the observed probability density functions. The result of this analysis is shown in Fig. S3, but the interpretation of the results as shown in the main text using all 23 models does not change.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Rio de Janeiro\u0026rsquo;s spring 2023 heatwave\u003c/h2\u003e \u003cp\u003eThe city of Rio de Janeiro experienced exceptionally high temperatures in both the austral winter and spring of 2023, peaking on November 18 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This record-breaking event became the highest daily maximum temperature on record at the Gale\u0026atilde;o International Airport weather station, reaching 41.3\u0026deg;C. The extreme heat event was also notable for its accompanying high specific humidity, which rose alongside temperature in the days leading up to November 18 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The combination of elevated temperature and humidity rendered the event a humid heat extreme, as measured by wet bulb temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). The coincidence of extreme dry and wet bulb temperatures is typical for extreme heat events in Rio de Janeiro, where there is a statistically significant positive correlation between daily maximum temperature and daily mean specific humidity (Fig. S4). This relationship is facilitated by the city\u0026rsquo;s abundant access to moisture from the coast and surrounding vegetation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eElevated temperatures were not limited to the city of Rio de Janeiro, but were spatially constrained by elevation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We explore the spatial patterns of the heatwave in data from the European Centre for Medium-Range Weather Forecasts (ERA5) reanalysis during the period of 1979\u0026ndash;2023 (Hersbach et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We see that hotspots in elevated temperatures were located throughout the coastal region surrounding Rio de Janeiro, with sharp declines across the mountainous terrain moving inland. These positive coastal temperature anomalies coincide with northerly surface wind anomalies. ERA5 estimates the daily maximum temperature on November 18 in the grid cell containing the Gale\u0026atilde;o International Airport weather station as 40.6\u0026deg;C, within the range of temperatures recorded throughout weather stations in the city (Fig. S1). This extreme event was also remarkable in length as measured by ERA5, as daily maximum temperatures were above the locally defined 90th percentile for eight consecutive days, and above the 99th percentile for the final three days of this period (percentiles calculated from ERA5 across the period from 1979\u0026ndash;2023). This multi-day interval of exceptional temperatures rendered it difficult for residents to find relief from the heat.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe maximum temperature during the event on November 18 coincided with other anomalous meteorological conditions (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e; for climatological values, see Fig. S5 in the Supplemental Materials). Positive geopotential height anomalies centered over Rio de Janeiro were consistent with an intensification of the South American Subtropical High, a semi-permanent anticyclonic circulation system off the Southeast coast of Brazil. The edge of this positive high pressure anomaly was collocated with the region of positive temperature anomalies that includes the city of Rio de Janeiro. Surface winds off the coast of Rio de Janeiro were anomalously northerly, while anomalous winds over the interior of South America enhanced the northerly South American Low Level Jet (Marengo et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Montini et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Positive specific humidity anomalies were present throughout Southeast and South Brazil, intersecting with an area of precipitation along the edge of the low pressure system to the south. The northern portion of the positive specific humidity anomaly was aligned with the positive geopotential height anomaly off the coast of Southeast Brazil. Widespread negative soil moisture anomalies occurred throughout most of Brazil, and the interior of South America more broadly, during this event. The large spatial coverage of these negative soil moisture anomalies was concurrent with Amazonian drought recorded during this time, inherited from the prior season (Espinoza et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Finally, positive SSTs of up to 2\u0026deg;C occurred along Rio de Janeiro\u0026rsquo;s coast. These spatial patterns are typical of extreme heat events during the spring season in the city of Rio de Janeiro, though the magnitudes of the anomalies in all of these variables are dramatically higher on November 18, 2023 than during other spring extreme heat events (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The most unique features of the November 18 event were the intensified northerly winds and the degree of inland penetration of positive specific humidity anomalies. Further, the positive local SST anomalies off the coast of Rio de Janeiro were particularly exceptional in intensity and spatial scale during this event, weakening the sea-air temperature contrast and sea-breeze. Outside of these specific distinctions, the event on November 18, 2023 was an intense example of a typical spring extreme heat event in the region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe time evolution of these variables throughout the month of November 2023 uncovers the temporal development of the extreme heat event (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Rising temperatures throughout the weeks leading up to November 18 were preceded by elevated geopotential heights at 500 hPa and associated atmospheric subsidence. This was accompanied by a rapid decline in soil moisture which was likely facilitated by the increased solar insolation associated with the persistent high pressure system and resulting extremely low precipitation from November 2-November 18. Given that the rainy season in Southeast Brazil typically begins in late-October to mid-November (Coelho et al., 2021; Latinovic et al., 2018; Marengo et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Liebmann and Mechoso 2011; Raia and Cavalcanti \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), this period of consecutive dry days was unusual. Indeed, this period totals 17 days in a row with less than 5 mm of rain per day, and this only happened during the month of November in one other year in the historical record from ERA5 between 1979\u0026ndash;2023 (2012). There was also a gradual increase in SST off the coast of Rio de Janeiro, though delayed compared to that of the local air temperature. Wind direction was highly variable on a daily scale, but became increasingly northerly during this same period. As air temperatures rose, specific humidity increased over the city. This was likely related to both local evaporation from the soil (co-occurring with declining soil moisture) and moisture advected from the coast and surrounding vegetation. The circulation specifically on November 18 directed wind in the larger region surrounding Rio de Janeiro to intensify the South American Low Level Jet, which can additionally increase moisture transport from the Amazon Basin to Southeast Brazil (Marengo et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Vera et al., \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Montini et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the surface moisture flux convergence was only stronger than the climatology in some grid cells within the northern and western areas of the city (Fig. S6). More generally, specific humidity was also able to build without reaching saturation due to the increasing temperatures (and the Clausius-Clapeyron relation). Finally, the heatwave was terminated when a two-day precipitation event occurred from November 19\u0026ndash;20. This precipitation induced a small decline in specific humidity and SST, as well as a rapid increase in soil moisture.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe evolution of the 2023 heatwave as shown above is reminiscent of that during the 2010 heatwave analyzed by Geirinhas and coauthors (2019). Those authors explain that the extreme heat event in the summer of 2010 was initiated by an SST anomaly over the eastern Pacific that triggered a Rossby wave train that in turn intensified the South Atlantic Subtropical High. Modulation of this high pressure system has been shown to be central to influencing weather in the city of Rio de Janeiro, and particularly temperatures there (Geirinhas et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Here we also observe an SST anomaly over the equatorial Pacific throughout the month of November and a resulting anomalous wave pattern ending over the South Atlantic High that became increasingly organized and strengthened during the two weeks before November 18 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; see Fig. S7 in the Supplemental Materials for maps of the climatologies and absolute magnitudes of these variables). This mechanism is similar to how El Ni\u0026ntilde;o generally influences temperatures in Southeast Brazil on longer timescales (Cai et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), and we confirm that there is a positive correlation between the ENSO state as quantified by the Ni\u0026ntilde;o3.4 index and the frequency of high heat days in the city of Rio de Janeiro in the austral spring season (Fig. S8). 2023 was characterized by a transition from La Ni\u0026ntilde;a to El Ni\u0026ntilde;o conditions, with the El Ni\u0026ntilde;o emerging in April-June 2023 and strengthening to a strong El Ni\u0026ntilde;o in the second half of 2023 (Becker et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The occurrence of El Ni\u0026ntilde;o conditions could have been responsible for initiating the wave train which set off the geopotential height anomalies over Rio de Janeiro. We note that similar wave trains driven by Pacific SST anomalies have been shown to influence weather in Southeast Brazil even during neutral ENSO states (Seth et al., 2015). Additionally, the instantaneous extreme temperature event and the preceding persistent dry conditions must also be linked to the synoptic weather in the area. Decreases in soil moisture and moisture fluxes from anomalous winds were central to the development of the heatwave in 2010, as they were in November 2023. These overlaps in the apparent drivers of the 2010 and 2023 heatwaves underscore that while last year\u0026rsquo;s spring event was unprecedented in its magnitude and unusual in its spring timing, it was not unique in its overall dynamics.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Historical and future changes in extreme heat\u003c/h2\u003e \u003cp\u003eExtreme heat events are becoming more frequent in the city of Rio de Janeiro and the timing of these events is shifting earlier in the calendar year. There has been a significant increase in the number of days above 30\u0026deg;C each year over the past 44 years throughout almost all of South America (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). Further, the number of 90th percentile days locally defined at each grid cell has also increased significantly throughout most of the region (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). In the city of Rio de Janeiro specifically, the number of 30\u0026deg;C days per year during the austral spring is increasing at a rate of 0.27 days/year (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). Relative to 1979, the city now experiences almost 12 additional days per year above 30\u0026deg;C during the spring season alone. Overall, the extreme heat season in Rio de Janeiro is broadening. As measured by the number of days between the first and last heatwave day of the season (a period of three or more consecutive days with daily maximum temperatures above 31.4\u0026deg;C), the extreme heat season has lengthened from 156 days in the 1979\u0026ndash;1980 season to 176 days in the 2022\u0026ndash;2023 season (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The broadening of the heat season is due primarily to more early season heatwave days, while the end date of the heat season has not changed significantly.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe distribution of maximum spring temperatures in the city of Rio de Janeiro has changed over the last four decades and is projected to continue to evolve in the future. We fit annual maximum spring temperatures from historical ERA5 reanalysis data and future projections from bias-corrected NASA Earth Exchange Global Daily Downscaled Projections (NEXGDDP) data (Thrasher et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, see Methods) using a Generalized Extreme Value (GEV) distribution. When comparing early and late historical periods from 1979\u0026ndash;1988 and 2014\u0026ndash;2023, respectively, the location parameter of the two GEV distributions has increased by 1.7\u0026deg;C (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The distribution of maximum austral spring temperatures in the city of Rio de Janeiro is also projected to continue shifting to higher values in the future, but the magnitude of this change is strongly dependent upon the future emissions pathway. The temperature distributions associated with mid-century periods (2041\u0026ndash;2060) under SSP2-4.5 and SSP5-8.5 future scenarios are similar to that of the last 10 years of observational data, with shifts in the location parameters of 0.1\u0026deg;C and 0.9\u0026deg;C for the two emissions trajectories, respectively. A larger change is projected by the end of the century (2081\u0026ndash;2100) under each emissions scenario. However, the end-of-century SSP5-8.5 scenario is distinctly separate from the other distributions, with the distribution location parameter 2.8\u0026deg;C higher than during the last 10 years.\u003c/p\u003e \u003cp\u003eThese changes to the distributions strongly influence the probability of an event with the intensity of the maximum temperature recorded on November 18, 2023. The probability density function fit to the projected annual maximum spring temperatures under each mid-century period using a GEV distribution yields a return period for an extreme temperature event with the daily maximum temperature at least 40.6\u0026deg;C in the city of Rio de Janeiro (analogous to the event on November 18, 2023 as measured by ERA5) of 51 years under SSP2-4.5 and 33 years under SSP5-8.5. By the end of the century under either emissions scenario, an event of this magnitude becomes much more likely, with return periods of 19 years or just 4 years under SSP2-4.5 and SSP5-8.5, respectively. Recent literature has suggested that the SSP5-8.5 scenario may not be realistic given our current socioeconomic, political, and physical landscape (Hausfather and Peters \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Burgess et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ritchie and Dowlatabadi \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, these results indicate that an austral spring heatwave of the magnitude experienced in the city of Rio de Janeiro on November 18 is projected to become much more frequent in the future, even under the more stringent SSP2-4.5 emission pathway. We must also note that it is difficult to evaluate whether the models are missing emerging factors that could increase the frequency and intensity of these extreme heat events \u0026ndash; such as Amazonian deforestation or declines in sea ice \u0026ndash; reducing their ability to capture the possible future spring temperature distributions in Rio de Janeiro.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe November 2023 heatwave in the city of Rio de Janeiro was a record-breaking event characterized by meteorological conditions largely typical of spring extreme temperature events, but exceptional in their magnitudes. Rising temperatures were associated with positive geopotential height anomalies and corresponding atmospheric subsidence which facilitated clear sky conditions and increased sensible heat flux at the surface. These high pressure anomalies centered over the South Atlantic Subtropical High were likely related to the strong 2023-24 El Ni\u0026ntilde;o event. The subsidence near Rio de Janeiro associated with the geopotential height anomalies also suppressed precipitation and facilitated evaporation from the land surface, leading to decreased soil moisture and increased specific humidity. Moisture was available from multiple sources to facilitate these humidity increases, as Rio de Janeiro is a coastal city and downwind of both the Amazon and more local vegetation. SSTs off the coast of Rio de Janeiro were also highly elevated in the days before the heatwave peak, reducing the potential for coastal cooling. Finally, the event was terminated on November 19 due to the evaporative cooling, shading, and mixing associated with the onset of precipitation. The combination of changes in circulation, land surface feedbacks, and atmosphere-ocean interactions generated the conditions for an exceptionally intense and persistent extreme heat event in the city of Rio de Janeiro.\u003c/p\u003e \u003cp\u003eThe risk of extreme heat in austral spring is increasing significantly in Rio de Janeiro. We find that extreme spring temperature events are becoming more frequent throughout Brazil, and the extreme heat season is starting earlier and lasting longer than in previous decades in the city of Rio de Janeiro. Further, extreme heat of the magnitude on November 18, 2023 may become much more likely by mid- and end-of-century periods. However, the absolute increase in the frequency of similar heatwaves is largely dependent upon our future emissions pathway.\u003c/p\u003e \u003cp\u003eThe November 2023 heatwave had devastating impacts, including loss of life. As our climate continues to change and extreme heat in the city of Rio de Janeiro continues to increase in intensity and frequency, we can expect more strain on human health and cascading socioeconomic impacts. This extreme heat event was notable not only in its intensity, but also in its persistence. Consecutive extreme heat days have been shown to have nonlinear impacts on human health, in Brazil and in other countries, as they prevent individuals, buildings, and critical electrical equipment from cooling down between heat events (Geirinhas et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Baldwin et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). More broadly, the direct impacts of heatwaves on hospitalizations throughout Brazil have been documented, with the largest effects occurring in long duration events (Zhao et al., \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These impacts of heatwaves on mortality are projected to increase, with particular consequences for elderly populations, especially if targeted adaptation measures are not put in place (Diniz et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Continuing to improve our understanding of how and when extreme heat occurs is thus essential as our climate continues to change. This is particularly true for locations such as Rio de Janeiro, which historically has not been a hotspot of extreme heat \u0026ndash; especially in the shoulder seasons \u0026ndash; and thus individuals may not be well acclimated to extreme temperatures then (Periard et al., 2015; Horowitz \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe meteorological conditions surrounding the extreme heat event analyzed here demonstrate the potential for compound hazards throughout Brazil. The identified circulation pattern that establishes the atmospheric blocking associated with heatwaves in Rio de Janeiro is also likely linked to heavy precipitation events in South Brazil, an extreme case of which occurred in May 2024 in center-north of Rio Grande do Sul, including the metropolitan area of Porto Alegre, displacing hundreds of thousands and killing at least 155 people (Rogero \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The temporal compounding of these extreme temperature and flooding events within Brazil has the potential to strain the country\u0026rsquo;s disaster management systems more than events occurring in isolation. Furthermore, the exceptional spatial area within Brazil that experienced anomalous heat in the November 2023 event, relative to 99th percentile heat events in the city of Rio de Janeiro, underscores the potential for spatially compounding heat that could lead to outsized impacts. Exploring how unprecedented global surface ocean and surface temperatures, along with regional features like the broader heat and drought across much of Brazil, may contribute to extreme heat in the city of Rio de Janeiro will be an important component to improving our understanding of these compound events\u0026rsquo; drivers, prediction capacity, and potential to change in the future.\u003c/p\u003e \u003cp\u003eThe evolving meteorological conditions associated with this heatwave were strongly impacted by the lack of precipitation in the first two weeks of November. This is particularly unexpected due to the fact that the active phase of the South American Monsoon System typically begins in late October or early November in this region (Marengo et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Liebmann and Mechoso 2011; Raia and Cavalcanti \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which is linked to an increase in convective activity in tropical South America in the warm season (Jones and Carvalho \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Observational and modeling studies suggest that the South American Monsoon System dry season is lengthening (Arias et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and that the onset of the active phase is delaying (Gomes et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pascale et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These trends are projected to continue to some degree in the future with further climate change, particularly in light of ongoing deforestation which contributes to regional drying trends in the Amazon and other areas of Brazil (Boisier et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Swann et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Given Rio de Janeiro is a city with abundant access to moisture due to its proximity to the coast and vegetation, the increasingly constrained active monsoon phase could lead to increased frequency and intensity of extreme humid heat in the spring season (Ivanovich et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Extensions of this work should be devoted to an exploration of these potential relationships.\u003c/p\u003e \u003cp\u003eThis work highlights the challenge of analyzing the drivers of weather extremes in such a climatically diverse city as Rio de Janeiro and emphasizes the need for future research to explore high resolution comparisons of mechanisms controlling the city\u0026rsquo;s microclimates. Differences between conditions recorded at individual weather stations within the city\u0026rsquo;s boundaries demonstrate the degree to which the dynamics of events in each neighborhood depend on the station\u0026rsquo;s location relative to the coast versus interior (Raymond and Mankin \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), elevation (Raymond et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Pepin et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and degree of urbanization (Kruger et al., 2024; Chakraborty et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Tan et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Higher temporal resolution analysis would also better capture sub-daily processes such as sea breeze and their effect on extreme heat throughout the city. Further, many of these mechanisms influencing the intracity variability of heat stress exposure only focus on the effect of differences in dry bulb temperature. Factoring in the spatial variation in humidity, solar insolation, and windspeed complicate understanding, but are essential for capturing humans\u0026rsquo; exposure to heat stress conditions. These intracity differences also meaningfully impact compound events with non-heat environmental hazards, such as floods, landslides, droughts, and air pollution, as well as how exposure to these hazards intersects with areas of social vulnerability. Future work should be devoted to investigating the different magnitudes of extreme heat and controlling mechanisms throughout Rio de Janeiro in order to inform targeted extreme heat adaptation plans for individual neighborhoods within the city.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCorrespondence to\u003c/h2\u003e \u003cp\u003eCatherine Ivanovich ([email protected])\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting Interests:\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e \u003cp\u003eS.J.C. conceived of the initial project concept. All co-authors contributed to study design, and C.I. performed the analysis. C.I. wrote the initial manuscript draft with the feedback and interpretation of all co-authors. All co-authors read and edited the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003eThis work was partially supported and funded by Columbia Global at Columbia University, \u0026ldquo;Simulation of Extreme Weather Events in Brazilian Megacities\u0026rdquo;, a Climate Hub | Rio Project. Climate Hub | Rio is a knowledge, research, and innovation hub that brings together experts from Brazil, Columbia University, and around the world to advance climate-related knowledge and action in Rio and Brazil. Direct funding for C. Ivanovich and R. Horton was provided by National Oceanic and Atmospheric Administration\u0026rsquo;s Regional Integrated Sciences and Assessments program, Grant NA15OAR4310147. A. H. Sobel acknowledges support from NSF Grant AGS-1933523. S. J. Camargo is partially supported by the NOAA grant NA23OAR43201600. The authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eData Availability:\u003c/h2\u003e \u003cp\u003eThe publicly available datasets used in this analysis are accessible via the following websites: HadISD, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metoffice.gov.uk/hadobs/hadisd/\u003c/span\u003e\u003cspan address=\"https://www.metoffice.gov.uk/hadobs/hadisd/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; ERA5, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; OISST, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncei.noaa.gov/products/optimum-interpolation-sst\u003c/span\u003e\u003cspan address=\"https://www.ncei.noaa.gov/products/optimum-interpolation-sst\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; and NEXGDDP, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6\u003c/span\u003e\u003cspan address=\"https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp-cmip6\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Station data from the Rio Alert System will be uploaded and accessible via a GitHub repository upon manuscript publication.\u003c/p\u003e \u003cp\u003e \u003cem\u003eCode Availability\u003c/em\u003e: All code used for the derivations, calculations, and data visualization will be made publicly available via a GitHub repository upon manuscript publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlvares CA, Stape JL, Sentelhas PC, De Moraes Gon\u0026ccedil;alves JL (2013) Modeling monthly mean air temperature for Brazil. Theoret Appl Climatol 113(3\u0026ndash;4):407\u0026ndash;427. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00704-012-0796-6\u003c/span\u003e\u003cspan address=\"10.1007/s00704-012-0796-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlvarez MS, Vera CS, Kiladis GN, Liebmann B (2016) Influence of the Madden Julian Oscillation on precipitation and surface air temperature in South America. Clim Dyn 46(1):245\u0026ndash;262. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00382-015-2581-6\u003c/span\u003e\u003cspan address=\"10.1007/s00382-015-2581-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArias PA, Fu R, Vera C, Rojas M (2015) A correlated shortening of the North and South American monsoon seasons in the past few decades. Clim Dyn 45(11):3183\u0026ndash;3203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00382-015-2533-1\u003c/span\u003e\u003cspan address=\"10.1007/s00382-015-2533-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaldwin JW, Dessy JB, Vecchi GA, Oppenheimer M (2019) Temporally Compound Heat Wave Events and Global Warming: An Emerging Hazard. Earth\u0026rsquo;s Future 7(4):411\u0026ndash;427. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2018EF000989\u003c/span\u003e\u003cspan address=\"10.1029/2018EF000989\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBecker E, L\u0026rsquo;Heureux M, Hu Z-Z, Kumar A (2024) ENSO and the tropical Pacific. In State of the Climate in 2023. Bull Amer Meteor Soc 105(8):S221\u0026ndash;S224. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/BAMS-D-24-0098.1\u003c/span\u003e\u003cspan address=\"10.1175/BAMS-D-24-0098.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBitencourt DP, Fuentes MV, Franke AE, Silveira RB, Alves MPA (2020) The climatology of cold and heat waves in Brazil from 1961 to 2016. Int J Climatol 40(4):2464\u0026ndash;2478. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/joc.6345\u003c/span\u003e\u003cspan address=\"10.1002/joc.6345\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBitencourt DP, Alves M, Shibuya L, De \u0026Acirc;ngelo EK, Cunha D, I., De Souza E, J. P (2021) Climate change impacts on heat stress in Brazil\u0026mdash;Past, present, and future implications for occupational heat exposure. Int J Climatol 41(S1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/joc.6877\u003c/span\u003e\u003cspan address=\"10.1002/joc.6877\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoisier JP, Ciais P, Ducharne A, Guimberteau M (2015) Projected strengthening of Amazonian dry season by constrained climate model simulations. Nat Clim Change 5(7):656\u0026ndash;660. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nclimate2658\u003c/span\u003e\u003cspan address=\"10.1038/nclimate2658\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess MG, Ritchie J, Shapland J, Pielke R (2020) IPCC baseline scenarios have over-projected CO2 emissions and economic growth. Environ Res Lett 16(1):014016. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/abcdd2\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/abcdd2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai W, McPhaden MJ, Grimm AM, Rodrigues RR, Taschetto AS, Garreaud RD et al (2020) Climate impacts of the El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation on South America. Nat Reviews Earth Environ 1(4):215\u0026ndash;231. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s43017-020-0040-3\u003c/span\u003e\u003cspan address=\"10.1038/s43017-020-0040-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCastelao RM, Barth JA (2006) Upwelling around Cabo Frio, Brazil: The importance of wind stress curl. Geophys Res Lett 33(3):2005GL025182. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2005GL025182\u003c/span\u003e\u003cspan address=\"10.1029/2005GL025182\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChakraborty T, Venter ZS, Qian Y, Lee X (2022) Lower Urban Humidity Moderates Outdoor Heat Stress. AGU Adv 3(5). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2022AV000729\u003c/span\u003e\u003cspan address=\"10.1029/2022AV000729\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. e2022AV000729\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng Y-T, Lung S-CC, Hwang J-S (2019) New approach to identifying proper thresholds for a heat warning system using health risk increments. Environ Res 170:282\u0026ndash;292. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.envres.2018.12.059\u003c/span\u003e\u003cspan address=\"10.1016/j.envres.2018.12.059\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCirino PH, F\u0026eacute;res JG, Braga MJ, Reis E (2015) Assessing the Impacts of ENSO-related Weather Effects on the Brazilian Agriculture. Procedia Econ Finance 24:146\u0026ndash;155. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2212-5671(15)00635-8\u003c/span\u003e\u003cspan address=\"10.1016/S2212-5671(15)00635-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCoelho CAS, De Souza DC, Kubota PY, Cavalcanti IFA, Baker JCA, Figueroa SN et al (2022) Assessing the representation of South American monsoon features in Brazil and U.K. climate model simulations. Clim Resil Sustain 1(1):e27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cli2.27\u003c/span\u003e\u003cspan address=\"10.1002/cli2.27\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCollins JM, Chaves RR, da Marques V S (2009) Temperature Variability over South America. J Clim 22(22):5854\u0026ndash;5869. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/2009JCLI2551.1\u003c/span\u003e\u003cspan address=\"10.1175/2009JCLI2551.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCordero Sim\u0026otilde;es dos, Reis N, Boiaski NT, Ferraz SET (2019) Characterization and Spatial Coverage of Heat Waves in Subtropical Brazil. Atmosphere 10(5):284. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/atmos10050284\u003c/span\u003e\u003cspan address=\"10.3390/atmos10050284\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorreio Braziliense (2023), November 18 Rio bate recorde de calor do ano neste s\u0026aacute;bado (18/11) com 42,5\u0026deg;C. Retrieved May 14, 2024, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.correiobraziliense.com.br/brasil/2023/11/6657444-rio-bate-recorde-de-calor-do-ano-neste-sabado-18-11-com-425-c.html\u003c/span\u003e\u003cspan address=\"https://www.correiobraziliense.com.br/brasil/2023/11/6657444-rio-bate-recorde-de-calor-do-ano-neste-sabado-18-11-com-425-c.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDereczynski C, Silva WL, Marengo J (2013) Detection and Projections of Climate Change in Rio de Janeiro, Brazil, 2013. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4236/ajcc.2013.21003\u003c/span\u003e\u003cspan address=\"10.4236/ajcc.2013.21003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiniz FR, Gon\u0026ccedil;alves FLT, Sheridan S (2020) Heat Wave and Elderly Mortality: Historical Analysis and Future Projection for Metropolitan Region of S\u0026atilde;o Paulo. Brazil Atmos 11(9):933. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/atmos11090933\u003c/span\u003e\u003cspan address=\"10.3390/atmos11090933\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDunn RJH (2019) HadISD version 3: monthly updates. Hadley Centre Technical Note\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEspinoza J-C, Jimenez JC, Marengo JA, Schongart J, Ronchail J, Lavado-Casimiro W, Ribeiro JVM (2024) The new record of drought and warmth in the Amazon in 2023 related to regional and global climatic features. Sci Rep 14(1):8107. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-024-58782-5\u003c/span\u003e\u003cspan address=\"10.1038/s41598-024-58782-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeron S, Cordero RR, Damiani A, Llanillo PJ, Jorquera J, Sepulveda E et al (2019) Observations and Projections of Heat Waves in South America. Sci Rep 9(1):8173. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-44614-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-44614-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFu R, Yin L, Li W, Arias PA, Dickinson RE, Huang L et al (2013) Increased dry-season length over southern Amazonia in recent decades and its implication for future climate projection. Proceedings of the National Academy of Sciences, 110(45), 18110\u0026ndash;18115. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1073/pnas.1302584110\u003c/span\u003e\u003cspan address=\"10.1073/pnas.1302584110\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeirinhas JL, Russo AC, Libonati R, Miralles DG, Sousa PM, Wouters H, Trigo RM (2022) The influence of soil dry-out on the record-breaking hot 2013/2014 summer in Southeast Brazil. Sci Rep 12(1):5836. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-022-09515-z\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-09515-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeirinhas Jo\u0026atilde;oL, Trigo RM, Libonati R, Coelho CAS, Palmeira AC (2018) Climatic and synoptic characterization of heat waves in Brazil. Int J Climatol 38(4):1760\u0026ndash;1776. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/joc.5294\u003c/span\u003e\u003cspan address=\"10.1002/joc.5294\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeirinhas Jo\u0026atilde;oL, Trigo RM, Libonati R, Castro LCO, Sousa PM, Coelho CAS et al (2019) Characterizing the atmospheric conditions during the 2010 heatwave in Rio de Janeiro marked by excessive mortality rates. Sci Total Environ 650:796\u0026ndash;808. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.scitotenv.2018.09.060\u003c/span\u003e\u003cspan address=\"10.1016/j.scitotenv.2018.09.060\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeirinhas Jo\u0026atilde;oL, Russo A, Libonati R, Trigo RM, Castro LCO, Peres LF et al (2020) Heat-related mortality at the beginning of the twenty-first century in Rio de Janeiro, Brazil. Int J Biometeorol 64(8):1319\u0026ndash;1332. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00484-020-01908-x\u003c/span\u003e\u003cspan address=\"10.1007/s00484-020-01908-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeirinhas Jo\u0026atilde;oL, Russo A, Libonati R, Sousa PM, Miralles DG, Trigo RM (2021) Recent increasing frequency of compound summer drought and heatwaves in Southeast Brazil. Environ Res Lett 16(3):034036. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/abe0eb\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/abe0eb\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGomes GD, Nunes AMB, Libonati R, Ambrizzi T (2022) Projections of subcontinental changes in seasonal precipitation over the two major river basins in South America under an extreme climate scenario. Clim Dyn 58:1147\u0026ndash;1169. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00382-021-05955-x\u003c/span\u003e\u003cspan address=\"10.1007/s00382-021-05955-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrimm AM (2019) Madden\u0026ndash;Julian Oscillation impacts on South American summer monsoon season: precipitation anomalies, extreme events, teleconnections, and role in the MJO cycle. Clim Dyn 53(1):907\u0026ndash;932. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00382-019-04622-6\u003c/span\u003e\u003cspan address=\"10.1007/s00382-019-04622-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHausfather Z, Peters GP (2020) Emissions \u0026ndash; the \u0026lsquo;business as usual\u0026rsquo; story is misleading. Nature 577(7792):618\u0026ndash;620. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/d41586-020-00177-3\u003c/span\u003e\u003cspan address=\"10.1038/d41586-020-00177-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Z, Dai A, Vuille M (2021) The Joint Impacts of Atlantic and Pacific Multidecadal Variability on South American Precipitation and Temperature. J Clim 34(19):7959\u0026ndash;7981. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JCLI-D-21-0081.1\u003c/span\u003e\u003cspan address=\"10.1175/JCLI-D-21-0081.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHersbach H, Bell B, Berrisford P, Hirahara S, Hor\u0026aacute;nyi A, Mu\u0026ntilde;oz-Sabater J et al (2020) The ERA5 global reanalysis. Q J R Meteorol Soc 146(730):1999\u0026ndash;2049. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/qj.3803\u003c/span\u003e\u003cspan address=\"10.1002/qj.3803\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHorowitz M (2016) Epigenetics and cytoprotection with heat acclimation. Journal of Applied Physiology (Bethesda, Md.: 1985), 120(6), 702\u0026ndash;710. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/japplphysiol.00552.2015\u003c/span\u003e\u003cspan address=\"10.1152/japplphysiol.00552.2015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang B, Liu C, Banzon V, Freeman E, Graham G, Hankins B et al (2021) Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST) Version 2.1. J Clim 34(8):2923\u0026ndash;2939. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JCLI-D-20-0166.1\u003c/span\u003e\u003cspan address=\"10.1175/JCLI-D-20-0166.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHughs E, Jeantet D (2023), November 15 It\u0026rsquo;s not yet summer in Brazil, but a dangerous heat wave is sweeping the country. Retrieved May 14, 2024, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apnews.com/article/brazil-heat-wave-climate-environment-wildfires-1e4714fb2c6566120c13cf4e2b657f7d\u003c/span\u003e\u003cspan address=\"https://apnews.com/article/brazil-heat-wave-climate-environment-wildfires-1e4714fb2c6566120c13cf4e2b657f7d\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInstituto Brasileiro de Geografia e Estat\u0026iacute;stica (2022) Demographic Census 2022. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sidra.ibge.gov.br/pesquisa/censo-demografico/demografico-2022/primeiros-resultados-populacao-e-domicilios\u003c/span\u003e\u003cspan address=\"https://sidra.ibge.gov.br/pesquisa/censo-demografico/demografico-2022/primeiros-resultados-populacao-e-domicilios\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIvanovich CC, Horton RM, Sobel AH, Singh D (2024) Subseasonal Variability of Humid Heat During the South Asian Summer Monsoon. Geophysical Research Letters, 51(6), e2023GL107382. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2023GL107382\u003c/span\u003e\u003cspan address=\"10.1029/2023GL107382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones C, Carvalho LMV (2013) Climate change in the South American Monsoon System: Present climate and CMIP5 projections. J Clim 26(17):6660\u0026ndash;6678. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JCLI-D-12-00412.1\u003c/span\u003e\u003cspan address=\"10.1175/JCLI-D-12-00412.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJornal Nacional (2023), November 18 Taylor Swift: segundo show \u0026eacute; adiado, por causa do calor extremo no Rio. Retrieved April 16, 2024, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://g1.globo.com/jornal-nacional/noticia/2023/11/18/taylor-swift-show-e-adiado-apos-morte-de-fa.ghtml\u003c/span\u003e\u003cspan address=\"https://g1.globo.com/jornal-nacional/noticia/2023/11/18/taylor-swift-show-e-adiado-apos-morte-de-fa.ghtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKr\u0026uuml;ger E, Gobo JPA, Tejas GT, da, Silva de Souza RM, Neto JBF, Pereira G et al (2024) The impact of urbanization on heat stress in Brazil: A multi-city study. Urban Climate, 53, 101827. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.uclim.2024.101827\u003c/span\u003e\u003cspan address=\"10.1016/j.uclim.2024.101827\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLanzante JR, Dixon KW, Nath MJ, Whitlock CE, Adams-Smith D (2018) Some Pitfalls in Statistical Downscaling of Future Climate. Bull Am Meteorol Soc 99(4):791\u0026ndash;803. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/BAMS-D-17-0046.1\u003c/span\u003e\u003cspan address=\"10.1175/BAMS-D-17-0046.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLatinović D, Chou SC, Rančić M, Medeiros GS, Lyra ADA (2019) Seasonal climate and the onset of the rainy season in western-central Brazil simulated by Global Eta Framework model. Int J Climatol 39(3):1429\u0026ndash;1445. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/joc.5892\u003c/span\u003e\u003cspan address=\"10.1002/joc.5892\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLemus-Canovas M, Insua-Costa D, Trigo RM, Miralles DG (2024) Record-shattering 2023 Spring heatwave in western Mediterranean amplified by long-term drought. Npj Clim Atmospheric Sci 7(1):1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41612-024-00569-6\u003c/span\u003e\u003cspan address=\"10.1038/s41612-024-00569-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLibonati R, Geirinhas JL, Silva PS, Dos Santos M, Rodrigues D, Russo JA, A., et al (2022) Drought\u0026ndash;heatwave nexus in Brazil and related impacts on health and fires: A comprehensive review. Ann N Y Acad Sci 1517(1):44\u0026ndash;62. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/nyas.14887\u003c/span\u003e\u003cspan address=\"10.1111/nyas.14887\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiebmann B, Mechoso CR, In C-P, Chang Y, Ding N-C, Lau RH, Johnson B, Wang, Yasunari T (2011) World Scientific Series on Asia-Pacific Weather and Climate (2nd edn., vol 5, 137\u0026ndash;157). WORLD SCIENTIFIC. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1142/9789814343411_0009\u003c/span\u003e\u003cspan address=\"10.1142/9789814343411_0009\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLyra GB, Correia TP, de Oliveira-J\u0026uacute;nior JF, Zeri M (2018) Theoret Appl Climatol 134(3):955\u0026ndash;965. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00704-017-2322-3\u003c/span\u003e\u003cspan address=\"10.1007/s00704-017-2322-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Evaluation of methods of spatial interpolation for monthly rainfall data over the state of Rio de Janeiro, Brazil\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarengo JA, Liebmann B, Grimm AM, Misra V, Silva Dias PL, Cavalcanti IFA et al (2012) Recent developments on the South American monsoon system. Int J Climatol 32(1):1\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/joc.2254\u003c/span\u003e\u003cspan address=\"10.1002/joc.2254\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarengo JA, Soares WR, Saulo C, Nicolini M (2004) Climatology of the Low-Level Jet East of the Andes as Derived from the NCEP\u0026ndash;NCAR Reanalyses: Characteristics and Temporal Variability. J Clim 17(12):2261\u0026ndash;2280. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/1520-0442(2004)017\u0026lt;2261:COTLJE\u0026gt;2.0.CO;2\u003c/span\u003e\u003cspan address=\"10.1175/1520-0442(2004)017%3C2261:COTLJE%3E2.0.CO;2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontini TL, Jones C, Carvalho LMV (2019) The South American Low-Level Jet: A New Climatology, Variability, and Changes. J Geophys Research: Atmos 124(3):1200\u0026ndash;1218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2018JD029634\u003c/span\u003e\u003cspan address=\"10.1029/2018JD029634\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMora C, Dousset B, Caldwell IR, Powell FE, Geronimo RC, Bielecki CR et al (2017) Global risk of deadly heat. Nat Clim Change 7(7):501\u0026ndash;506. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nclimate3322\u003c/span\u003e\u003cspan address=\"10.1038/nclimate3322\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNahlik MJ, Chester MV, Pincetl SS, Eisenman D, Sivaraman D, English P (2017) Building Thermal Performance, Extreme Heat, and Climate Change. J Infrastruct Syst 23(3):04016043. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1061/(asce)is.1943-555x.0000349\u003c/span\u003e\u003cspan address=\"10.1061/(asce)is.1943-555x.0000349\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEl Ni\u0026ntilde;o 2023 | El Ni\u0026ntilde;o/La Ni\u0026ntilde;a Watch \u0026amp; NASA JPL, Retrieved PDO (2024), January 23 May 14, 2024, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sealevel.jpl.nasa.gov/data/el-nino-la-nina-watch-and-pdo/el-nino-2023\u003c/span\u003e\u003cspan address=\"https://sealevel.jpl.nasa.gov/data/el-nino-la-nina-watch-and-pdo/el-nino-2023\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen B (2023), December 27 Brazilian Taylor Swift Fan Died Of Heat Exhaustion At Rio Concert. Retrieved April 16, 2024, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.forbes.com/sites/britneynguyen/2023/12/27/brazilian-taylor-swift-fan-died-of-heat-exhaustion-at-rio-concert/\u003c/span\u003e\u003cspan address=\"https://www.forbes.com/sites/britneynguyen/2023/12/27/brazilian-taylor-swift-fan-died-of-heat-exhaustion-at-rio-concert/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePalma ED, Matano RP (2009) Disentangling the upwelling mechanisms of the South Brazil Bight. Cont Shelf Res 29(11):1525\u0026ndash;1534. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.csr.2009.04.002\u003c/span\u003e\u003cspan address=\"10.1016/j.csr.2009.04.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePascale S, Carvalho LMV, Adams DK, Castro CL, Cavalcanti IFA (2019) Current and Future Variations of the Monsoons of the Americas in a Warming Climate. Curr Clim Change Rep 5(3):125\u0026ndash;144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40641-019-00135-w\u003c/span\u003e\u003cspan address=\"10.1007/s40641-019-00135-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePepin N, Bradley RS, Diaz HF, Baraer M, Caceres EB, Forsythe N et al (2015) Elevation-dependent warming in mountain regions of the world. Nat Clim Change 5(5):424\u0026ndash;430. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/nclimate2563\u003c/span\u003e\u003cspan address=\"10.1038/nclimate2563\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eP\u0026eacute;riard JD, Racinais S, Sawka MN (2015) Adaptations and mechanisms of human heat acclimation: Applications for competitive athletes and sports. Scand J Med Sci Sports 25(Suppl 1):20\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/sms.12408\u003c/span\u003e\u003cspan address=\"10.1111/sms.12408\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePowis CM, Byrne D, Zobel Z, Gassert KN, Lute AC, Schwalm CR (2023) Observational and model evidence together support wide-spread exposure to noncompensable heat under continued global warming. Sci Adv 9(36):eadg9297. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/sciadv.adg9297\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.adg9297\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerkins SE, Pitman AJ, Holbrook NJ, McAneney J (2007) Evaluation of the AR4 Climate Models\u0026rsquo; Simulated Daily Maximum Temperature, Minimum Temperature, and Precipitation over Australia Using Probability Density Functions. J Clim 20(17):4356\u0026ndash;4376. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JCLI4253.1\u003c/span\u003e\u003cspan address=\"10.1175/JCLI4253.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaia A, Cavalcanti IFA (2008) The Life Cycle of the South American Monsoon System. J Clim 21(23):6227\u0026ndash;6246. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/2008JCLI2249.1\u003c/span\u003e\u003cspan address=\"10.1175/2008JCLI2249.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaymond C, Mankin JS (2019) Assessing present and future coastal moderation of extreme heat in the Eastern United States. Environ Res Lett 14(11):114002. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/ab495d\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/ab495d\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaymond C, Matthews T, Horton RM (2020) The emergence of heat and humidity too severe for human tolerance. Sci Adv 6(19):eaaw1838. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1126/sciadv.aaw1838\u003c/span\u003e\u003cspan address=\"10.1126/sciadv.aaw1838\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaymond C, Waliser D, Guan B, Lee H, Loikith P, Massoud E et al (2022) Regional and Elevational Patterns of Extreme Heat Stress Change in the US. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/ac7343\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/ac7343\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Environmental Research Letters\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRegoto P, Dereczynski C, Chou SC, Bazzanela AC (2021) Observed changes in air temperature and precipitation extremes over Brazil. Int J Climatol 41(11):5125\u0026ndash;5142. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/joc.7119\u003c/span\u003e\u003cspan address=\"10.1002/joc.7119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRehbein A, Ambrizzi T (2023) ENSO teleconnections pathways in South America. Clim Dyn 61(3):1277\u0026ndash;1292. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00382-022-06624-3\u003c/span\u003e\u003cspan address=\"10.1007/s00382-022-06624-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie J, Dowlatabadi H (2017) Why do climate change scenarios return to coal? Energy. 140:1276\u0026ndash;1291. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.energy.2017.08.083\u003c/span\u003e\u003cspan address=\"10.1016/j.energy.2017.08.083\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRogero T (2024), May 19 Brazil counts cost of worst-ever floods with little hope of waters receding soon. The Guardian. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.theguardian.com/world/article/2024/may/19/brazil-floods-toll\u003c/span\u003e\u003cspan address=\"https://www.theguardian.com/world/article/2024/may/19/brazil-floods-toll\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRogers CDW, Ting M, Li C, Kornhuber K, Coffel ED, Horton RM et al (2021) Recent Increases in Exposure to Extreme Humid-Heat Events Disproportionately Affect Populated Regions. Geophys Res Lett 48(19). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2021GL094183\u003c/span\u003e\u003cspan address=\"10.1029/2021GL094183\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShimizu MH, Ambrizzi T (2016) MJO influence on ENSO effects in precipitation and temperature over South America. Theoret Appl Climatol 124(1):291\u0026ndash;301. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00704-015-1421-2\u003c/span\u003e\u003cspan address=\"10.1007/s00704-015-1421-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShreevastava A, Raymond C, Hulley GC (2023) Contrasting Intraurban Signatures of Humid and Dry Heatwaves over Southern California. J Appl Meteorol Climatology 62(6):709\u0026ndash;720. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JAMC-D-22-0149.1\u003c/span\u003e\u003cspan address=\"10.1175/JAMC-D-22-0149.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSistema Alerta Rio da Prefeitura do Rio de Janeiro (2024) Meteorological Data. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://alertario.rio.rj.gov.br/download/dados-meteorologicos/\u003c/span\u003e\u003cspan address=\"https://alertario.rio.rj.gov.br/download/dados-meteorologicos/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Sistema IBGE (2024) Recupera\u0026ccedil;\u0026atilde;o Autom\u0026aacute;tica - SIDRA. (n.d.). Retrieved April 21, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sidra.ibge.gov.br/pesquisa/censo-demografico/demografico-2022/primeiros-resultados-populacao-e-domicilios\u003c/span\u003e\u003cspan address=\"https://sidra.ibge.gov.br/pesquisa/censo-demografico/demografico-2022/primeiros-resultados-populacao-e-domicilios\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSon J-Y, Gouveia N, Bravo MA, de Freitas CU, Bell ML (2016) The impact of temperature on mortality in a subtropical city: effects of cold, heat, and heat waves in S\u0026atilde;o Paulo, Brazil. Int J Biometeorol 60(1):113\u0026ndash;121. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00484-015-1009-7\u003c/span\u003e\u003cspan address=\"10.1007/s00484-015-1009-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSwann ALS, Longo M, Knox RG, Lee E, Moorcroft PR (2015) Future deforestation in the Amazon and consequences for South American climate. Agric For Meteorol 214\u0026ndash;215. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agrformet.2015.07.006\u003c/span\u003e\u003cspan address=\"10.1016/j.agrformet.2015.07.006\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan J, Zheng Y, Tang X, Guo C, Li L, Song G et al (2010) The urban heat island and its impact on heat waves and human health in Shanghai. Int J Biometeorol 54(1):75\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00484-009-0256-x\u003c/span\u003e\u003cspan address=\"10.1007/s00484-009-0256-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThrasher B, Wang W, Michaelis A, Melton F, Lee T, Nemani R (2022) Sci Data 9(1):262. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41597-022-01393-4\u003c/span\u003e\u003cspan address=\"10.1038/s41597-022-01393-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. NASA Global Daily Downscaled Projections, CMIP6\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUnited Nations Department of Economic and Social Affairs Population Division (2022) Population of Urban Agglomerations with 300,000 Inhabitants or More in 2018, by country, 1950\u0026ndash;2035 (thousands). Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://population.un.org/wup/Download/\u003c/span\u003e\u003cspan address=\"https://population.un.org/wup/Download/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUS EPA O (2021), February 4 Climate Change Indicators: Heat Waves [Reports and Assessments]. Retrieved April 23, 2024, from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.epa.gov/climate-indicators/climate-change-indicators-heat-waves\u003c/span\u003e\u003cspan address=\"https://www.epa.gov/climate-indicators/climate-change-indicators-heat-waves\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVecellio DJ, Wolf ST, Cottle RM, Kenney WL (2022) Evaluating the 35\u0026deg;C wet-bulb temperature adaptability threshold for young, healthy subjects (PSU HEAT Project). J Appl Physiol 132(2):340\u0026ndash;345. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1152/japplphysiol.00738.2021\u003c/span\u003e\u003cspan address=\"10.1152/japplphysiol.00738.2021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVera C, Baez J, Douglas M, Emmanuel CB, Marengo J, Meitin J et al (2006) The South American Low-Level Jet Experiment. Bull Am Meteorol Soc 87(1):63\u0026ndash;78. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/BAMS-87-1-63\u003c/span\u003e\u003cspan address=\"10.1175/BAMS-87-1-63\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilby RL, Kasei R, Gough KV, Amankwaa EF, Abarike M, Anderson NJ et al (2021) Monitoring and moderating extreme indoor temperatures in low-income urban communities. Environ Res Lett 16(2):024033. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1748-9326/abdbf2\u003c/span\u003e\u003cspan address=\"10.1088/1748-9326/abdbf2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou Y, Ting M, Biasutti M (2024) Climate warming contributes to the record-shattering 2022 Pakistan rainfall. Npj Clim Atmospheric Sci 7(1):1\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41612-024-00630-4\u003c/span\u003e\u003cspan address=\"10.1038/s41612-024-00630-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Q, Li S, Coelho MSZS, Saldiva PHN, Hu K, Huxley RR et al (2019) The association between heatwaves and risk of hospitalization in Brazil: A nationwide time series study between 2000 and 2015. PLoS Med 16(2):e1002753. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pmed.1002753\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmed.1002753\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5355924/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5355924/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs extreme heat has not historically been a major hazard for the city of Rio de Janeiro, the November 2023 Heatwave magnitude and timing were staggering. Here we conduct a case study of reanalysis data and high-resolution projections to explore the event drivers and characterize the evolving extreme heat risk in the city of Rio de Janeiro. We find that the heatwave was associated with atmospheric blocking, potentially linked to the 2023-24 El Ni\u0026ntilde;o event. Soil moisture declines increased surface sensible heat flux, and elevated sea surface temperatures reduced coastal cooling. The heatwave was preceded by weeks of suppressed precipitation and terminated by the onset of rain. We also find a significant historical increase in the frequency of high heat days throughout Brazil and a lengthening of the heat season in the city of Rio de Janeiro. The frequency of the city\u0026rsquo;s austral spring heat extremes is expected to increase further in the future, highly dependent upon our future emissions pathway. These results emphasize the rapidly emerging risk for extreme heat in the city of Rio de Janeiro.\u003c/p\u003e","manuscriptTitle":"Physical Drivers of the November 2023 Heatwave in Rio de Janeiro","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2025-04-11 18:34:24","doi":"10.21203/rs.3.rs-5355924/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2024-12-02 23:04:22","doi":"10.21203/rs.3.rs-5355924/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"47bfe582-1e52-48b6-b95a-8ebaf8ff8a8d","owner":[],"postedDate":"April 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46803541,"name":"Earth and environmental sciences/Climate sciences"},{"id":46803542,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science"},{"id":46803543,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics"},{"id":46803544,"name":"Scientific community and society/Social sciences/Climate change/Climate and earth system modelling"}],"tags":[],"updatedAt":"2025-02-07T04:53:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-11 18:34:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-5355924","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5355924","identity":"rs-5355924","version":["v2"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}