General Circulation and Earth System Models are the most advanced tools for investigating climate responses to future scenarios of greenhouse gas emissions, playing the role of projecting the climate throughout the century. Nevertheless, climate projections are model-dependent and may show systematic biases, requiring a bias correction for any further application. Here, we provide a dataset based on an ensemble of 19 bias-corrected CMIP6 climate models projections for the Brazilian territory based on the SSP2-4.5 and SSP5-8.5 scenarios. We used the Quantile Delta Mapping approach to bias-correct daily time-series of precipitation, maximum and minimum temperature, solar net radiation, near-surface wind speed, and relative humidity. The bias-corrected dataset is available for both historical (1980–2013) and future (2015–2100) simulations at a 0.25° × 0.25° spatial resolution. Besides the gridded product, we provide area-averaged projections for 735 catchments included in the Catchments Attributes for Brazil (CABra) dataset. The dataset provides important variables commonly used in environmental and hydroclimatological studies, paving the way for the development of high-quality research on climate change impacts in Brazil.

River-aquifer interactions play a crucial role in water availability, influencing environmental flows and impacting climate dynamics. Where groundwater tables lie below river water levels, stream water can infiltrate into the underlying aquifer, reducing streamflow. However, the prevalence of these “losing” rivers remains poorly understood due to limited national-wide in situ observations. Here we analyze water levels in 17,972 wells across Brazil to show that most of them (55%) lie below nearby stream surfaces, implying that these nearby streams are likely seeping into the subsurface. Our results demonstrate the widespread potential for stream water losses into underlying aquifers in many regions of the country, especially in areas with extensive groundwater pumping. Our direct observations underscore the importance of conjunctively managing groundwater and surface water, and highlight the widespread risk of streamflow losses to aquifers, which could impact global water access and ecosystems that rely on rivers.

The Budyko water balance is a fundamental concept in hydrology that links aridity to how precipitation is divided between evapotranspiration and streamflow. While the model is powerful, its ability to explain temporal changes and the influence of human activities and climate change is limited. Here we introduce a causal discovery algorithm to explore deviations from the Budyko water balance, attributing them to human interventions such as agricultural activities and snow dynamics. Our analysis of 1342 catchments across the U.S. and Great Britain reveals distinct patterns: in the U.S., snow fraction and irrigation alter the Budyko water balance predominantly through changes in aridity-streamflow relationships, while in Great Britain, deviations are primarily driven by changes in precipitation-streamflow relationships, notable in catchments with high cropland percentage. By integrating causal analysis with the Budyko water balance, we enhance understanding of how human activities and climate dynamics affect water balance, offering insights for water management and sustainability in the Anthropocene.

Extreme rainfall events are expected to intensify with global warming, posing significant challenges to both human and natural environments. Despite the importance of such assessments, they are unevenly widespread across the globe. Here, using bias corrected climate simulations of the latest phase of the Coupled Model Intercomparison Project (CMIP6), we provide a comprehensive assessment on how different rainfall events are expected to change across Brazil. Specifically, (a) we explored the projected changes in both intensity and frequency of rainfall events belonging to the right‐tail of the rainfall distribution using a non‐parametric approach, and (b) quantified how rainfall events associate with different return periods are expected to intensify, using a parametric approach. We found that extreme rainfall events will become more frequent and intense by the end of the century, with averaged projected changes for rainfall exceeding the historical rainfall quantile q _0.99 of nearly 100% and 10% on frequency and intensity, respectively. Non‐extreme rainfall events, in contrast, are expected to be less frequent, aligning with the compensation hypothesis. For instance, Brazilian 100‐year rainfall are anticipated to intensify, on average, 17% and 31% under the moderate and the highest CMIP6 emission scenarios, respectively. Finally, our findings suggest that frequency, rather than intensity, dictates the projected changes of rainfall. We believe that the evidence gathered here will certainly contribute to not only an improved understanding of Brazilian rainfall events but also to a better comprehension of the different rainfall properties, their interplay and how the different ways of assessing them may affect climate studies.