Interest in region-specific assessments of droughts and the need to optimise water resources planning and allocation on a local scale via additional investments in water infrastructures is emerging as novel management initiatives to build drought resilience. In this study, a novel two-step regularization procedure that combines statistical rotation with support vector machine regression (SVMR) is employed to assess and identify hydrological regions in Brazil associated with global climate teleconnection patterns (e.g., ENSO, PDO, etc.). To enhance realistic drought impacts assessments, region-specific attributes of drought and the climate modes associated with its variability and characteristics are studied using standardised precipitation index (SPI) and reanalysis data (MERRA). Compared to other regions, results show that drought variability and its occurrence are relatively higher in the extreme north, north-east, and south of Brazil. The predominance of extreme drought events shows that more than 50% of Brazil was affected by the 1998/1999 drought while areas under droughts in recent times fluctuated between 25% in 2012 and 70% in 2015. Results also show significant association of ENSO (e.g., R2 = 28%) and PDO (e.g., R2 = 18%) with drought indicators in several climatic hot spots. The synthesis of climate modes as predictors of droughts in the SVMR scheme highlights the influence and importance of the Pacific and Atlantic oceans on drought evolutions in Brazil. The MERRA-derived drought indicator extracted this influence better (e.g., r = 0.72) than the SPI and appears to be a more suitable drought metric to understand the impacts of global climate on extreme events in the region.