The present study analyzes the impacts of global warming of 1.5ºC, 2ºC, and 4ºC above pre-industrial levels in the Brazilian territory. Climate change projected among the different global warming levels has been analyzed for rainfall, temperature and extreme climate indices. The projections are derived from the global climate model HadGEM3-A, from the High-End cLimate Impacts and eXtremes (HELIX) international project, from the United Kingdom, forced by sea surface temperature and sea ice concentration of a subset of six CMIP5 (Coupled Model Intercomparison Project phase 5) global climate models and considering the RCP 8.5 (Representative Concentration Pathways) emissions scenario throughout the 21st century. Projections indicate robust differences in regional climate characteristics. These differences include changes: in the minimum and maximum air temperature close to the surface to all the country’s regions, in extremes of heat, particularly in northern Brazil, in the occurrence of heavy rainfall (Southern and Southeastern regions), and in the probability of droughts and rain deficits in some regions (Northern and Northeastern Brazil).

A reliable prognosis of extreme precipitation events in the tropics is arguably challenging to obtain due to the interaction of meteorological systems at various time scales. A pivotal component of the global climate variability is the so-called intraseasonal oscillations, phenomena that occur between 20 and 100 days. The Madden–Julian Oscillation (MJO), which is directly related to the modulation of convective precipitation in the equatorial belt, is considered the primary oscillation in the tropical region. The aim of this study is to diagnose the connection between the MJO signal and the regional intraseasonal rainfall variability over tropical Brazil. This is achieved through the development of an index called Multivariate Intraseasonal Index for Tropical Brazil (MITB). This index is based on Maximum Covariance Analysis (MCA) applied to the filtered daily anomalies of rainfall data over tropical Brazil against a group of covariates consisting of: outgoing longwave radiation and the zonal component u of the wind at 850 and 200 hPa. The first two MCA modes, which were used to create the \({ MITB}_1\) and \({ MITB}_2\) indices, represent 65 and 16 % of the explained variance, respectively. The combined multivariate index was able to satisfactorily represent the pattern of intraseasonal variability over tropical Brazil, showing that there are periods of activation and inhibition of precipitation connected with the pattern of MJO propagation. The MITB index could potentially be used as a diagnostic tool for intraseasonal forecasting.

The intraseasonal variability over South America is investigated using a multivariate index based on maximum covariance analysis (MCA). This technique identifies the correlation patterns of two different data sets. The Climate Prediction Center (CPC) grid precipitation over South America and the tropical means (15°N–15°S) of outgoing long wave radiation (OLR) and zonal wind component at 850 and 200 hPa (National Oceanic and Atmospheric Administration (NOAA) and National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research (NCAR) reanalysis, respectively) are used in the study. The MCA was applied to these data sets and a Multivariate Intraseasonal Rainfall Index for South America for South America (MIRI.SA) was constructed based on the phase and amplitude vectorial projections of the first two modes. Composites of selected cases when the amplitude was above a threshold are discussed for different phases of the Madden–Julian Oscillation (MJO). Phases (8+1) and (4+5) present strong convection anomalies over the maritime continent and South America, but with opposite signs. These patterns represent the dominant mode of precipitation over South America. Phases (2+3) and (6+7) are transient and related to the secondary mode of precipitation over South America. A space‐phase diagram calculated using the MIRI.SA index at different lags represents the positive precipitation locations over both South America and the Equatorial Pacific. The construction of this diagram in near real time could be used for monitoring precipitation extremes over different areas of South America.

Agriculture is the world’s main economic activity. According to the Intergovernmental Panel on Climate Change, this activity is expected to be impacted by drought. In the Northeast region of Brazil (NEB), most agricultural activity is carried out by small rural communities. Local socio-economic data were analyzed using multivariate statistical techniques in this study to determine agricultural sensitivity to drought events (SeA) and agricultural vulnerability to drought extremes (VaED). The climate data used to develop the risk factor (R_drought) were the drought indicator with the Standard Precipitation Index (SPI) and the average number of drought disasters from 1991 to 2012. Conditional probability theory was applied to determine agricultural vulnerability to drought extremes (VaED). Characterization of the risk of agricultural drought using the proposed methodology showed that the rainy season presents high risk values in the central region, covering areas of the states of Ceará, Piauí, Pernambuco and Rio Grande do Norte, as well as all areas of the semi-arid region. The risk ranged from high to medium. The results also indicated that part of the south of Bahia and the west of Pernambuco have areas of extreme agro-climatic sensitivity. Consequently, these states have an extreme degree of climate vulnerability during the region’s rainy season.

This study presents the first comprehensive regionalization of Angola’s rainfall and estimates onset and cessation of the rainy season using Satellite-based Precipitation Estimates (SPEs). During 2021 to 2023, data from stations of the National Institute of Meteorology and Geophysics of Angola (INAMET) were used to evaluate the performance of two SPE products: Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and Integrated Multi-satellite Retrievals for GPM (IMERG). Rainfall in Angola is better represented by SPE products in higher-altitude regions than in coastal areas. Among them, CHIRPS outperforms IMERG in terms of correlation, statistical distribution, and temporal consistency. Importantly, CHIRPS also provides a longer record (1981–2023). Using K-means clustering on 43 years of CHIRPS data, we identified seven distinct precipitation regimes across Angola. Most regions show austral summer rainfall peaks, while the Northwest uniquely peaks in autumn. Using Gaussian fits to daily precipitation, three regions (Central-East, Central-South, Southeast) exhibit a unimodal regime with a summer maximum. Four regions (Northwest, Northeast, Central-West, Southwest) show a transitional regime with two peaks, one between spring–summer and another in autumn, and a higher likelihood of extreme events in autumn. The rainy-season onset ranges from August 1 (Northeast) to October 7 (Southwest), and the end ranges from May 9 (Southwest) to June 3 (Northeast). The Northeast has the longest rainy season, starting in winter; 68 days later, the Southeast, the shortest, starts in spring. An opposite pattern is observed in autumn: rainfall ends first in the Southeast, and 25 days later it ends in the Northeast. Future work should compare onset and cessation estimates at the grid-point scale using multiple methods across the seven regions. Graphical Abstract