Southeast Brazil has great economic importance for Brazil and is highly vulnerable to extreme events like floods and droughts. Studies have shown an increase of temperature in this region. Using a new detection and attribution framework (Ribes et al., 2017, https://doi.org/10.1007/s00382-016-3079-6) and Coupled Model Intercomparison Project Phase 5 models, this change is found to be largely due to greenhouse gases. We estimate that greenhouse gases contribute a warming of 0.95 to 1.5 °C to the observed warming trend of 1.1 °C between 1955 and 2004. Temperature changes from natural and nongreenhouse gas anthropogenic forcing are estimated to be small over this period. Results are robust using different time windows. Using the Community Earth System Model ensembles to evaluate the impacts of internal variability, observational and model error shows that most uncertainty arises from model error.

In March 2020, an extreme rainfall in Baixada Santista, Brazil, led to a series of landslides affecting more than 2800 people and resulting losses exceeding USD 43 million. This attribution study compared extreme rainfall in two large ensembles of the UK Met Office Hadley Centre HadGEM3-GA6 model that represented the event with and without the effects of anthropogenic climate change. Antecedent rainfall conditions on two different timescales are considered, namely extreme 60-day rainfall (Rx60day) which relates to the soil moisture conditions and extreme 3-day rainfall (Rx3day) which represents landslide triggering heavy rainfall. In the scenario including both natural and human-induced factors the antecedent 60 day rainfall became 74% more likely, while the short-term trigger was 46% more likely. The anthropogenic contribution to changes in rainfall accounted for 20–42% of the total losses and damages. The greatest economic losses occurred in Guarujá (42%), followed by São Vicente (30%) and Santos (28%). Landslides were responsible for 47% of the homes damaged, 85% of the homes destroyed, all reported injuries, and 51% of the deaths associated with heavy rainfall. Changes in land cover and urbanization showed a pronounced increase in urbanized area in Guarujá (107%), São Vicente (61.7%) and Santos (36.9%) and a reduction in farming area. In recent years, the region has experienced an increase in population growth and a rise in the proportion of irregular and/or precarious housing in high-risk areas. Guarujá has the highest number of such dwellings, accounting for 34.8%. Our estimates suggest that extreme precipitation events are having shorter return periods due to climate change and increased urbanization and population growth is exposing more people to these events. These findings are especially important for decision-makers in the context of disaster risk reduction and mitigation and adaptation to climate change.

Droughts in the Amazon region have the potential to generate severe socio‐environmental impacts in addition to having the ability to interfere with the long‐term carbon cycle, thus affecting global climate. The 2015/2016 drought that occurred in this region, associated with an El Niño, was considered a record‐breaking event in terms of unprecedented warming and the largest extent of the drought affected areas. Anthropogenic influence on the probability and intensity of this drought was assessed using two ensembles of the Met Office's HadGEM3‐GA6 model. One ensemble was driven only with natural forcings and the other also included anthropogenic forcings. This analysis found that the intensity and probability of the 2015/2016 Amazon drought likely increased due to anthropogenic influence. The reliability of the model to represent the precipitation of the study area was assessed by comparing it with the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) product (R² = 0.81). Results indicate that anthropogenic forcings altered the drought intensity of 2015/2016 in the Amazon and increased the risk of this event by about four times with a confidence interval ranging from 2.7 to 4.7. We conclude that anthropogenic emissions threaten the functioning of the Amazon forest due to increased likelihood of extreme droughts.

A recent drought in Southeast Brazil affected the Cantareira reservoirs system (CRS), which supplies water to São Paulo megacity, and raised concerns on the impacts that climate change may have on the water budget at the watershed scale. We propose to identify the particular and summed contributions of key climatic variables on the variability of the water budget in Jaguari basin, the main CRS tributary, using forcing–response relationships derived from climate projections and individual elasticities of variables to temperature. Besides, we investigated whether hydrological effects of the anomalous drought were comparable to patterns simulated in the future climate. A thoroughly calibrated hydrological model for evapotranspiration (ET) and discharge (Q) was used to address our questions. We found opposite impacts in the future mostly driven by rainfall changes: under increasing (decreasing) rainfall, the mean ET increased (decreased) up to +25% (−10%) and Q increased (decreased) by +90% (−50%). Higher carbon dioxide concentrations strongly depleted the stomatal conductance, and thus the mean ET, which in turn increased the mean Q in near proportions. Major critical impacts for water management are suggested by the results. Even with a small reduction of precipitation, the discharge patterns in the drought event were replicated at similar intensities.

The spatial range of near-surface air temperature average and trends for Southeast Brazil in recent decades motivated us to investigate the causality of local vegetation and other geophysical controls at the regional scale to explain the spatial variability of the average maximum and minimum temperature (Tmax and Tmin). We used measurements from 52 weather stations between 1985 and 2010. Using linear regression, NDVI and cloud cover were significant to explain spatial variability of Tmax and Tmin. With the Generalized Additive Model (GAM), we improved temperature-dependent relationships with regional geophysical controls, and local scale NDVI. The modeling of Tmax and Tmin showed non-linear and combined relationships with geographical position (lat,lon) jointly expressing the effects of zonality and continentality, and NDVI at distances of 300 m and 3000 m. For Tmin, geographical position and altitude responded with an amplitude of ≃5 °C each, and NDVI with ≃3 °C. Similarly, the geographical position and altitude were significant for Tmax, with an amplitude of ≃5 °C each, and cloud cover with ≃3.5 °C. Our findings help to clarify the local scale controls of near-surface air temperature and stress the need to increase resilience against adversities of global climate change and increasing urbanization, by providing metrics to predict the effects of nature-based solutions within the urban space.

Climate change-related weather and extreme events are increasing in intensity and frequency(Nath and Palmer 2026), affecting infectious disease transmission globally(Mora et al. 2022). Dengue, a climate-sensitive vector-borne disease, to which over half the world’s population is at risk of infection, has expanded its geographical range over recent decades(Lim et al. 2025; Bhatt et al. 2013). The 2023/24 season marked the largest ever dengue outbreak year in the Americas, coinciding with the hottest year on record there(“Instituto Nacional de Meteorologia - INMET,” n.d.). Here, we use statistical models to investigate the Brazil 2023/24 dengue season and attribute how anthropogenic climate change impacted it. We analyze >20 years of dengue data across >5000 municipalities and find that observed temperature anomalies in 2023/24 increased dengue incidence in Brazil by 34% (95% CI: 22, 43%) accounting for population immunity. There was spatial heterogeneity across the country where in municipalities of southern Brazil temperatures were pushed into optimal thermal conditions for dengue transmission. For the best fitting model we found that in northern Brazil, 2023/24 temperatures became too high for effective transmission, resulting in lower dengue incidence compared to a counterfactual scenario without anthropogenic climate change, though with larger uncertainty. We test the generalizability of our model to high altitude areas in Mexico, where dengue has been expanding. Our work empirically demonstrates how a climate-change-related temperature anomaly led to the range expansion and growth of dengue across variable ecological and socio-economic settings, with implications for preparedness, adaptation, mitigation, and resilience planning.