Instead of using the traditional space-time Fourier analysis of filtered specific atmospheric fields, a normal-mode decomposition method was used to analyze South American intraseasonal variability (ISV). Intraseasonal variability was examined separately in the 30–90-day band, 20–30-day band, and 10–20-day band. The most characteristic structure in the intraseasonal time-scale, in the three bands, was the dipole-like convection between the South Atlantic Convergence Zone (SACZ) and the central-east South America (CESA) region. In the 30–90-day band, the convective and circulation patterns were modulated by the large-scale Madden–Julian oscillation (MJO). In the 20–30-day and 10–20-day bands, the convection structures were primarily controlled by extratropical Rossby wave trains. The normal-mode decomposition of reanalysis data based on 30–90-day, 20–30-day, and 10–20-day ISV showed that the tropospheric circulation and CESA–SACZ convective structure observed over South America were dominated by rotational modes (i.e., Rossby waves, mixed Rossby-gravity waves). A considerable portion of the 30–90-day ISV was also associated with the inertio-gravity (IGW) modes (e.g., Kelvin waves), mainly prevailing during the austral rainy season. The proposed decomposition methodology demonstrated that a realistic circulation can be reproduced, giving a powerful tool for diagnosing and studying the dynamics of waves and the interactions between them in terms of their ability to provide causal accounts of the features seen in observations.

This study introduces four univariate regional indices to improve the representation of intraseasonal rainfall variability across South America throughout the year, focusing on Brazil. These indices are constructed using two distinct approaches: the linear Empirical Orthogonal Functions (EOF) method and the unsupervised machine‐learning Self‐Organizing Maps (SOM) technique. Both methods are applied to Outgoing Longwave Radiation (OLR) and precipitation‐filtered anomalies in the 30–90‐day band over the South American domain. Results demonstrate that regional indices provide valuable insights into intraseasonal South American rainfall variability, including phase and strength, compared to global indices of the Madden‐Julian Oscillation (MJO). Despite being computed using only the South American domain, the regional indices capture the tropical‐tropical MJO teleconnection through the zonal wavenumber‐1 structure. The diversity in amplitude and evolution of precipitation, primarily influenced by tropical‐extratropical teleconnections through Rossby wave trains, is more evident when using the non‐linear SOM index. The regional indices also accurately measure the impacts of intraseasonal variability on extreme precipitation events over Brazil. Case studies, such as the 2013/2014 summer drought episode, highlight this ability, when a deficient rainy season severely affected the Southeast Region of Brazil, impacting agricultural production and hydroelectric power generation. During this episode, the regional indices show agreement between drought periods and suppressed precipitation phases, while global indices indicate an inactive MJO phase. These findings underscore the effectiveness of regional indices in capturing intraseasonal variability, offering significant implications for extreme weather prediction and their impacts on South American water resources and socio‐economic activities.

The skill of the diverse-based precipitation products is investigated in comparison with HYBAM rain-gauge observations. The performance of three remote sensing-based datasets (the Climate Hazards Group InfraRed Precipitation with Station, CHIRPS, the Multi-Source Weighted-Ensemble Precipitation, MSWEP, and the Tropical Rainfall Measuring Mission, TRMM) is evaluated considering different timescales for the Amazon Basin, an area with widely heterogeneous precipitation. The analysis considered seasonal, intraseasonal and diurnal timescales through the computation of the cluster analysis, the seasonality index, the Kling-Gupta Efficiency metric, spectral analysis and composing technique. CHIRPS has the lowest performance to represent the rainfall in the northwest portion of the basin, where it underestimated the mean precipitation compared to the other bases. In this region, the other remote sensing-based (TRMM and MSWEP databases) compared to HYBAM also showed considerable variability and misrepresentation of the intraseasonal rainfall. In general, all databases perform better in the north and eastern portions of the basin compared to HYBAM. The comparison of the diurnal rainfall cycle between remote sensing-based data and the field campaigns of TRMM-LBA and GoAmazon, and the Huayao station in the Andes was also evaluated. At the diurnal timescale, MSWEP predates the time of the rainfall peak, but represents the magnitude of the precipitation well compared with TRMM. This study is necessary to warn about the importance of a more complete and objective assessment of the data before considering it for applications in different precipitation studies, mainly in regions with high rainfall heterogeneity like the Amazon Basin.