The main objective of this study is to assess the ability of several high-resolution satellite-based precipitation estimates to represent the Precipitation Diurnal Cycle (PDC) over Brazil during the 2014–2018 period, after the launch of the Global Precipitation Measurement satellite (GPM). The selected algorithms are the Global Satellite Mapping of Precipitation (GSMaP), The Integrated Multi-satellitE Retrievals for GPM (IMERG) and Climate Prediction Center (CPC) MORPHing technique (CMORPH). Hourly rain gauge data from different national and regional networks were used as the reference dataset after going through rigid quality control tests. All datasets were interpolated to a common 0.1° × 0.1° grid every 3 h for comparison. After a hierarchical cluster analysis, seven regions with different PDC characteristics (amplitude and phase) were selected for this study. The main results of this research could be summarized as follow: (i) Those regions where thermal heating produce deep convective clouds, the PDC is better represented by all algorithms (in term of amplitude and phase) than those regions driven by shallow convection or low-level circulation; (ii) the GSMaP suite (GSMaP-Gauge (G) and GSMaP-Motion Vector Kalman (MVK)), in general terms, outperforms the rest of the algorithms with lower bias and less dispersion. In this case, the gauge-adjusted version improves the satellite-only retrievals of the same algorithm suggesting that daily gauge-analysis is useful to reduce the bias in a sub-daily scale; (iii) IMERG suite (IMERG-Late (L) and IMERG-Final (F)) overestimates rainfall for almost all times and all the regions, while the satellite-only version provide better results than the final version; (iv) CMORPH has the better performance for a transitional regime between a coastal land-sea breeze and a continental amazonian regime. Further research should be performed to understand how shallow clouds processes and convective/stratiform classification is performed in each algorithm to improve the representativity of diurnal cycle.

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.

Monitoring rainfall in the Brazilian Legal Amazon (BLA), which comprises most of the largest tropical rainforest and largest river basin on the planet, is extremely important but challenging. The size of the area and land cover alone impose difficulties on the operation of a rain gauge network. Given this, we aimed to evaluate the performance of nine databases that estimate rainfall in the BLA, four from gridded analyses based on pluviometry (Xavier, CPC, GPCC and CRU), four based on remote sensing (CHIRPS, IMERG, CMORPH and PERSIANN-CDR), and one from reanalysis (ERA5Land). We found that all the bases are efficient in characterizing the average annual cycle of accumulated precipitation in the BLA, but with a predominantly negative bias. Parameters such as Pearson’s correlation (r), root-mean-square error (RMSE) and Taylor diagrams (SDE), applied in a spatial analysis for the entire BLA as well as for six pluviometrically homogeneous regions, showed that, based on a skill ranking, the data from Xavier’s grid analysis, CHIRPS, GPCC and ERA5Land best represent precipitation in the BLA at monthly, seasonal and annual levels. The PERSIANN-CDR data showed intermediate performance, while the IMERG, CMORPH, CRU and CPC data showed the lowest correlations and highest errors, characteristics also captured in the Taylor diagrams. It is hoped that this demonstration of hierarchy based on skill will subsidize climate studies in this region of great relevance in terms of biodiversity, water resources and as an important climate regulator.

Precipitation predictions in the sub‐seasonal timescale are very important for several sectors such as agriculture and energy in regions of South America that are very much affected by precipitation extremes, both excess and lack of rain. The aim of the present study is to investigate the ability of two S2S project models (ECMWF and NCEP) to detect the Southern Hemisphere teleconnections in model hindcasts and the associated anomalous precipitation over South America. The period of analyses is 1999–2010 for the austral summer season (December–January–February). Both models represented adequately the Southern Annular Mode (SAM) pattern in predictions up to 4 weeks ahead and the Pacific South America (PSA) pattern up to 3 weeks. Atmospheric variables of observed extreme cases of SAM were well predicted by the two models, 2 and 3 weeks in advance. The models predicted well atmospheric variables in observed extreme cases of PSA, 2 weeks in advance and with less intensity in the third week. Precipitation anomaly signals associated with these modes were well predicted 2 weeks in advance, although with different intensities. The good ability of the models hindcast in predicting teleconnection patterns and precipitation anomalies over South America provides more confidence to use predictions at sub‐seasonal timescale.

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.