Daniela Carneiro Rodrigues

Understanding the variability and evolution of precipitation extremes associated with climate change is essential to support adaptation strategies, risk management, and civil protection. In this context, this study investigates the climatology, observed trends, and future projections of precipitation extremes in the Southeast Atlantic Hydrographic Basin (SAHB), a region of great socioeconomic relevance in Brazil. Daily precipitation data from 244 pluviometric and meteorological stations of the National Water and Basic Sanitation Agency (ANA) and the National Institute of Meteorology (INMET) were used, covering the period from 1980 to 2023. Precipitation extremes were evaluated using standardized indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). Trend detection and assessment in the time series were performed using the non‐parametric Mann–Kendall test and Sen's slope estimator. For the evaluation of future conditions, simulations from the EC‐Earth3 Earth system model, part of CMIP6, were used, considering the SSP2‐4.5 and SSP5‐8.5 emission scenarios for the period 2030–2100. The results indicate a significant increase in the mean rainfall intensity and a longer duration of dry periods (+4 days/decade), suggesting greater irregularity in the regional precipitation regime. Extreme precipitation events over short periods exhibit spatially heterogeneous intensification, with greater intensity in coastal and mountainous regions. Future projections indicate intensified extreme rainfall (up to +41 mm/decade in precipitation above the 95th percentile) and longer dry spells, particularly under the high‐emission scenario, suggesting greater susceptibility to hydrometeorological disasters and significant impacts on water security and territorial planning across the basin.

Future climate projections suitable for local impact and adaptation studies require the use of regional climate models (RCMs) to downscale global climate models (GCMs). In the process of preparing the Eta RCM to produce projections using the CMIP6 GCMs, the Eta model has been integrated to produce a climate run. The objective of this work is to evaluate the Eta RCM simulation of the South American climate driven by ERA5 reanalysis. The model is set up at 20-km horizontal resolution with 38 vertical layers, covering the entire South American continent and parts of the adjacent oceans. The 30-year integration starts at 0000 UTC on January 1st, 1984, and ends on the same date in 2015. The climatology is assessed in four trimesters corresponding to the austral seasons: DJF (summer), MAM (autumn), JJA (winter), and SON (spring). The maximum precipitation is simulated in DJF over the Northern South America (NSA), extending into the South America Monsoon Region (SAM) and Southeast South America (SES), corresponding to the position of the South Atlantic Convergence Zone (SACZ). The minimum precipitation (< 1mm/day) is correctly simulated in JJA, mainly over SAM. Although the major features of the precipitation seasonal variation in the continent are reproduced by the simulation, there is a large overestimation over the Northwestern South America (NWS), a lack of precipitation band in the Intertropical Convergence Zone near the northern coast over the equatorial Atlantic Ocean, and an underestimation over SAM and SES. The seasonal variation of the 2-m temperature is well simulated over the continent; however, there is an overestimation in the region around Paraguay, Bolivia, northern Argentina, and western Brazil, and an underestimation in the eastern part of Brazil and southern Argentina. Climate extreme indices are calculated on a seasonal basis. The model correctly simulates the mean patterns of consecutive dry days (CDD), heavy precipitation days (R30mm), and total precipitation from very wet days (R95p). The CDD and R30mm trends are generally well simulated across seasons, but the positive R95p trend observed is not captured by the simulations. Similarly, the model correctly simulates the mean patterns of temperature extremes, including the warmest day (TXx) and warmest night (TNx). While the simulation shows an increasing trend in TXx across much of SAM in all seasons, the observations show greater spatial and seasonal variability in areas with a positive trend in TXx. The warm spell duration index (WSDI) shows a positive trend across the entire continent in both observations and simulations. The evaluation of extreme drought and rainy years in three major river basins will be presented.

The downscaling of climate change projections is crucial for impact and adaptation studies, as such studies require higher spatial resolution data than those provided by global CMIP6 models. EC-Earth3 is therefore used as a driving model for dynamic downscaling with the Eta/INPE regional model over SA. In this study, we analyze projected trends under the SSP5-8.5 scenario, focusing on the 2-m air temperature (2 m) and precipitation until the end of the 21st century. Changes are evaluated from 2015 onwards relative to the 1985–2014 climatology and analyzed seasonally (DJF, MAM, JJA, SON). In the near future period, an increasing trend in precipitation is projected over the South Atlantic Convergence Zone (SACZ) during DJF, as well as over northern SA during MAM, while a reduction in precipitation is projected over most of the domain, particularly over the Amazon region. From 2025 onwards, precipitation reductions over the Amazon are projected to predominate in nearly all seasons, while positive changes are projected over parts of northeastern, southeastern, and central SA. This pattern persists in subsequent decades. From 2060 onwards, precipitation reduction over the SACZ exceeds 300 mm per season in SON and DJF. By the end of the century, a strong negative change is projected across the entire SACZ region. In contrast, a persistent positive change is projected over southern Brazil, likely associated with an increase in extreme precipitation events. This contrast tends to enhance the spatial variability of climate over SA and suggests changes in the South American monsoon system and the continental hydrological cycle. Regarding temperature, initial chances do not exceed ±1 °C over most of central SA. From 2025 onwards, positive changes range from +1 to +3 °C across most of the domain. The warming signal intensifies over time, exceeding +4 °C from 2035 onwards during SON. From the 2040s onward, positive temperature changes dominate across the model domain under the SSP5-8.5 scenario. Projected mean temperature increases exceed +6 °C over the central region from the 2050s onwards. By the end of the century, temperature increase range from +6 to +8 °C over most of Brazil. The main agricultural production region in Central Brazil exhibits the highest warming rates. The SON season presents the largest temperature increases and precipitation anomalies.

This study aimed to evaluate the performance of the Eta Regional Climate Model in reproducing the seasonal climate over South America for the rainy season from November until April, with emphasis on the Madeira, São Francisco, and Paraná river basins. For this purpose, a 10-year set of 6-month range seasonal hindcasts was produced using the Eta Regional Climate Model at 20-km horizontal resolution driven by the CFSv2 forecasts. In addition to dynamical downscaling, the precipitation and 2-meter temperature were statistically downscaled by applying a Quantile Mapping bias correction. The Eta model forecasts reasonably reproduced the precipitation and temperature patterns in the region, with some errors that were reduced by the statistical downscaling. Precipitation skill scores are higher in the northern and central areas of the continent. Although it has shown mixed performance for extreme events—low in the Paraná basin and limited but useful in the Madeira and São Francisco basins—the dynamical-statistical system developed with the Eta model shows higher skill and added value over the driver model, indicating potential to support water resources management in South America.

The Madeira River, a major tributary of the Amazon River, often undergoes severe flood and drought conditions. This study seeks to investigate the climate processes associated with the opposing extreme precipitation events in the Madeira River basin and to relate them to river discharge variability based on a flood awareness dataset. Despite the uncertainty in the observational datasets, the annual precipitation cycle exhibits a rainy season from November to March. A significant result is the high correlation between the rainy season variability in the Madeira River basin and the sea surface temperature (SST) anomalies in the tropical North Atlantic Ocean and the southwestern South Atlantic Ocean. This result indicates that improving the Atlantic SST representation in climate modeling allows for capturing extreme precipitation events in the region. In addition to this impact, certain Madeira River tributaries present significant climate trends. The river discharge variability reveals an increase in hydrological extremes in recent years in the upper sector, but more significantly, in the lower basin, where it has reduced by more than 400 m³/s per decade. These findings highlight the need to improve in situ data and climate and hydrological modeling, with a focus on describing the intense climate variability and trends in river discharges.