Brazil is increasingly affected by extreme weather events due to climate change, with pronounced regional differences in temperature and precipitation patterns. The southeast region is particularly vulnerable, frequently experiencing severe droughts and extreme heatwaves linked to atmospheric blocking events and intense rainfall episodes driven by the South Atlantic Convergence Zone (SACZ). These phenomena contribute to recurring climate-related disasters. The country’s heavy reliance on hydropower heightens its susceptibility to droughts, while growing evidence points to intensifying dry spells and wildfires across multiple regions, threatening agricultural output and food security. Urban areas, particularly, are experiencing more frequent and severe heatwaves, posing serious health risks to vulnerable populations. This study investigates the links between global teleconnection indices and synoptic-scale systems, specifically blocking events and SACZ activity, and their influence on Brazil’s extreme heat, drought conditions, and river flow variability over the past 30 to 40 years. By clarifying these interactions, the research aims to enhance understanding of how large-scale atmospheric dynamics shape climate extremes and to assess their broader implications for water resource management, energy production, and regional climate variability.

The primary goal of this study is to introduce the initial phase of developing an impact-based forecasting system for extreme heatwaves, utilizing a novel multivariate index which, at this early stage, already employs a combination of a statistical approach and physical principles related to human body water loss. This system also incorporates a mitigation plan with hydration-focused measures. Since 1990, heatwaves have become increasingly frequent and intense across many regions worldwide, particularly in Europe and Asia. The main health impacts of heatwaves include organ strain and damage, exacerbation of cardiovascular and kidney diseases, and adverse reproductive effects. These consequences are most pronounced in individuals aged 65 and older. Many national meteorological services have established metrics to assess the frequency and severity of heatwaves within their borders. These metrics typically rely on specific threshold values or ranges of near-surface (2 m) air temperature, often derived from historical extreme temperature records. However, to our knowledge, only a few of these metrics consider the persistence of heatwave events, and even fewer account for relative humidity. In response, this study aims to develop a globally applicable normalized index that can be used across various temporal scales and regions. This index incorporates the potential health risks associated with relative humidity, accounts for the duration of extreme heatwave events, and is exponentially sensitive to exposure to extreme heat conditions above critical thresholds of temperature. This novel index could be more suitable/adapted to guide national meteorological services when emitting warnings during extreme heatwave events about the health risks on the population. The index was computed under two scenarios: first, in forecasting heatwave episodes over a specific temporal horizon using the WRF model; second, in evaluating the relationship between the index, mortality data, and maximum temperature anomalies during the 2003 summer heatwave in Spain. Moreover, the study assessed the annual trend of increasing extreme heatwaves in Spain using ERA5 data on a climatic scale. The results show that this index has considerable potential as a decision-support and health risk assessment tool. It demonstrates greater sensitivity to extreme risk episodes compared to linear evaluations of extreme temperatures. Furthermore, its formulation aligns with the physical mechanisms of water loss in the human body, while also factoring in the effects of relative humidity.

This study aims to develop a heat wave forecasting system using a new multivariate index that encompasses hydration-related mitigation measures. Heatwaves have increasingly occurred with greater frequency and intensity in various regions worldwide, particularly in Europe and Asia since 1990, although they are not exclusive to these areas. The principal health effects of heatwaves on populations include heat-related illnesses and fatalities, cardiovascular and kidney diseases, as well as adverse reproductive effects. These detrimental impacts are widespread and commonly affect individuals aged 65 and above. Many nations have established metrics to assess the prevalence of this occurrence within their borders. These metrics typically utilize specific thresholds and/or temperature ranges at a height of 2 meters, which denote extreme percentiles of values from past records. While some of these metrics consider the persistence of the phenomenon, few take into account the relative humidity. It is noteworthy that, in most instances, the temperature thresholds lead to a linear escalation in conditions posing a risk to the population. This can result in a biased perception of the actual level of risk involved. To thoroughly evaluate the health hazards associated with heatwaves, it is essential to acknowledge the considerable variability in global climate, as well as the diverse responses of living organisms to extreme temperature and humidity conditions. Furthermore, factors such as individuals' gender, race, age, pre-existing medical conditions, and geographical location should be taken into account.This study is divided into several components to reach a comprehensive solution. The first step involves determining the monthly distribution curve of accumulated daily maximum temperatures for each grid point of the ERA 5 data. After completing this process, machine learning models must be developed to calibrate the temperature values to the percentile of the cumulative distribution. Subsequently, the temperature value exceeding 95% of the distribution will be applied to this coefficient Coef = (eTpe*Ur)/1000, where Tpe is the value of the distribution that exceeds 95% and Ur is the relative humidity. These adjusted values will then be used to compute the normalized index I=(Coef-0.022)/9.7, accounting for the exponential temperature increase and providing weightage to the relative humidity. Upon establishment of these functions, a time series of the index value will be generated. This value will be multiplied by the hours of the day during which the index deviates from zero, facilitating the evaluation of its correlation with hospitalization and mortality data related to diseases such as thrombosis, which may be linked to heat waves. The results of this phase will be presented at the Niterói region in Rio de Janeiro, Brazil, during the upcoming congress. Moreover, according to previous analyzes, since 2010 the frequency and intensity of heat waves have increased, being apparently modulated by Enso events and also by indices developed at LAMMOC/UFF related to anomalies of sea surface temperature of the Equatorial Atlantic Ocean and the South Atlantic Convergence Zone. Furthermore, the index data will subsequently undergo validation based on body water loss rates and their impact on blood viscosity fluctuations.

The main purpose of this study is to develop a heatwave impact-based forecasting system using a new multivariate index, that also encompasses a mitigation action plan with hydration-related measures. Since 1990, heatwaves have become more frequent and intense in various regions worldwide, particularly in Europe and Asia. The principal health effects of heatwaves include organs' strain and damage, complications of cardiovascular and kidney diseases, as well as adverse reproductive effects. These detrimental impacts are widespread and commonly affect individuals aged 65 and above. Many nations have established metrics to assess the prevalence of this occurrence within their borders. These metrics typically use specific threshold values and/or ranges of the near-surface (2 m) air temperature, usually denoted by the extreme values from past records. To the best of our knowledge, only some of these metrics take into account the persistence of the phenomenon and few consider the relative humidity. It is noteworthy that in most of these metrics the temperature thresholds lead to a linear escalation of the conditions posing a risk to the population, which may lead to a misperception of the actual level of risk involved. To thoroughly evaluate the health hazards associated with heatwaves, it is essential to consider the climate variability and change at regional and local scales, as well as the diverse responses of living organisms to extreme (and long-lasting) temperature and humidity conditions. Factors such as individuals' sex, ancestry, age, pre-existing medical conditions, and geographical location should be considered too. The first step of this study consisted of the characterization of the monthly Cumulative Distribution Function of the daily maximum near-surface air temperature (TX) in summer, in recent climate. We used the ERA5-Land reanalysis dataset and performed the analysis for each grid point, considering 1960-1990 as baseline period. Subsequently, in order to compute the index, the temperature values exceeding the 95th percentile (TX95p) were subjected to a normalized scaling function whose values grow exponentially with the magnitude of the temperature and also depend on the ambient relative humidity. The resulting index values range from 0 to 1, only being greater than zero when the temperature exceeds TX95p. To calibrate the index, we considered the hours of the day during which the index deviates from zero and its correlation with hospitalization and mortality data, mainly related to cardiovascular diseases such as thrombosis. The preliminary work concerned the Region of Murcia, in Spain. The index was validated in the period 2000-2022. Results show the sensitivity of the index, which displays its largest values in the summer of 2022, coinciding with the high number of heat-related deaths observed that year in Spain. Future research will be focused on index calibration and validation in other regions which are also subjected to extreme heat conditions.

Rising global average temperatures, as a consequence of climate change, have worsened the occurrences of extreme weather events, causing disruptions in rainfall patterns around the world. In Brazil, such effects are already observed with the increase of heat waves, floods, droughts, and wildfires. The correlation between disruptions in precipitation patterns and fires is complex, nevertheless, the intensity, frequency, and duration of drought events have significant impacts on fuel flammability and fire behavior. Drought monitoring is particularly relevant in Brazil, where the vast majority of forest fires have an anthropogenic ignition and prolonged dry periods favor such fires to spread out of control. The Standardized Precipitation Index (SPI) is one of the most important tools used to evaluate precipitation variability, offering simple yet robust statistical information on the distribution, duration, and frequency of rainfalls and, consequently, droughts. The SPI uses precipitation as input data to standardize the deviation of cumulated rainfall from the mean of historical precipitation, detecting water deficit (negative values) or water surplus (positive values) for a given location. In doing so, this index allows direct spatial comparability between arid and humid regions. This is an advantageous characteristic when drought analysis is applied to a country with different regional rainfall regimes, such as Brazil. The applicability of SPI as a source of drought prediction was investigated by observing its performance with historical climate simulations of the 6th phase of the Model for Interdisciplinary Research on Climate (MIROC6) and the fifth generation ECMWF atmospheric reanalysis of the global climate, ERA5. The direct comparison of the SPI data, employing the climatology extending from 1980-2014 in Brazil, derived both from the climate simulation model and the reanalysis data - which combines observations and models – has provided valuable insights. Preliminary results show an overall consistency in the calculated indexes from both sources, which are in line with seasonal regional rainfall patterns in Brazil. On average, the SPI indexes recognize water deficits for the North-east, north of the South-east and central regions of Brazil. During the months of winter, both indexes detect droughts in these regions, with ERA-5 SPI index registering severe droughts in central Brazil. These results suggest that the SPI index calculated using the reanalysis data seems to register droughts with greater severity and longer duration, identifying more precisely periods with little to no rainfall, whilst the SPI derived from the MIROC6 simulation data, although able to acceptably identify and delimitate droughts, records less severity for the same period. These findings are important to recognize the MIROC6-derived SPI index as a valuable tool in drought prediction. However, they also highlight the necessity of acknowledging the limitations of the model regarding the severity of droughts. The understanding and prediction of precipitation anomalies is fundamental to coping with the impacts of climate change on water resources, agriculture, and biodiversity, guiding mitigation and adaptation strategies in Brazil.