We analyse seasonal and annual trends of extreme indices of air temperature and precipitation over Brazil during the period 1961–2018. The main goal is to investigate whether the climate is changing and if so, to explore if there is any marked seasonality in such changes. The daily observed datasets of maximum and minimum temperatures, and precipitation, are provided by the Brazilian National Institute of Meteorology and National Water Agency. We use the Sen Curvature and Mann‐Kendall statistical tests to compute the magnitudes and to evaluate the statistical significance of climate extremes trends, respectively. The results show that the warm extremes frequency of occurrence is increasing significantly while the opposite occurs for cold extremes, which reveals a very consistent and widespread warming over Brazil. The highest increases in warm extremes occur during austral spring and summer while for the cold extremes the greatest decreases are observed during austral winter. Unlike temperature, precipitation extremes show heterogeneous signals for most of the country. In Northeast Brazil, there are changes towards a drier climate, especially in summer and autumn. In the Southern region, the climate is becoming wetter, with a reduction in consecutive dry days, especially in spring. For the other regions, there is no strong clear change sign, but both positive and negative precipitation extreme trends, without statistical significance (mostly in Southeast Region).

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Health determinants might play an important role in shaping the impacts related to long-term disasters such as droughts. Understanding their distribution in populated dry regions may help to map vulnerabilities and set coping strategies for current and future threats to human health. The aim of the study was to identify the most vulnerable municipalities of the Brazilian semiarid region when it comes to the relationship between drought, health, and their determinants using a multidimensional index. From a place-based framework, epidemiological, socio-economic, rural, and health infrastructure data were obtained for 1135 municipalities in the Brazilian semiarid region. An exploratory factor analysis was used to reduce 32 variables to four independent factors and compute a Health Vulnerability Index. The health vulnerability was modulated by social determinants, rural characteristics, and access to water in this semiarid region. There was a clear distinction between municipalities with the highest human welfare and economic development and those municipalities with the worst living conditions and health status. Spatial patterns showed a cluster of the most vulnerable municipalities in the western, eastern, and northeastern portions of the semiarid region. The spatial visualization of the associated vulnerabilities supports decision making on health promotion policies that should focus on reducing social inequality. In addition, policymakers are presented with a simple tool to identify populations or areas with the worst socioeconomic and health conditions, which can facilitate the targeting of actions and resources on a more equitable basis. Further, the results contribute to the understanding of social determinants that may be related to medium- and long-term health outcomes in the region.

Regional Climate Models (RCMs) provide climate information required for the evaluation of vulnerability, impacts, and adaptation at ner scales than their global driving models. As they explicitly resolve the basic conservation and state equations, they solve physics with more detail, conserving teleconnection of larger scales provided by GCMs. In South America (SA), the regional simulations have been historically evaluated principally on climatological aspects, but the representativeness of extremes still needs a deeper assessment. This study aims to analyze three RCMs driven by different GCMs: RegCM4-7, REMO2015, and Eta in the CORDEX SA region with focus on their capacity to reproduce extreme ETCCDI historical indices of daily precipitation and extreme temperature. Rx5day, CDD, TXx, and TNn were evaluated regarding the historical spatio-temporal variability and trends and climate change projections for the 2071-2099 period in the RCP8.5 were provided.The historical behavior of RCMs (1981RCMs ( -2005 was compared with two gridded products: CPC and Agrometeorological indicators derived from ERA5 reanalysis, previously compared with records from meteorological stations to assess potential observational biases. The results show that the highest observational uncertainty is observed in the regions with more scarce surface stations (North and West of SA) and with complex topography, being more pronounced in the precipitation-based indices. We found that RCMs generally show more agreement in the spatial variability than in the inter-annual variability for all the indices and SA regions. When analyzing the observed trends, all models better reproduce the long term variability of extreme temperature indices. More disagreement is present for Rx5day and CDD indices trends, including substantial spatial heterogeneities in both magnitude and sign of tendency. Climate change projections exhibited signi cant agreement to warmer conditions in TXx and TNn, but precipitation signals differed between RCMs and the driving GCM within each regional model. Maximum dry spells are expected to increase in almost all SA regions whereas the climate change signals in extreme precipitation events are more consistent over southeastern SA (northern and southwestern SA) with positive (negative) changes by the end of the century.

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