Eastern Northeast Brazil (ENEB) generally experiences a high variability in precipitation in the dry season, with amplitudes that can overcome 500 mm. The understanding of this variability can help in mitigating the socioeconomic issues related to the planning and management of water resources this region, which is highly vulnerable to drought. This work aims to assess spatio-temporal variability of precipitation during the dry season and investigate the relationships between climate phenomena and drought events in the ENEB, using univariate (Spearman correlation) and multivariate statistical techniques, such as Principal Component Analysis, Cluster Analysis, and Maximum Covariance Analysis. The results indicate that the variability of precipitation in the dry season can be explained mainly (62%) by local physical conditions and climate conditions have a secondary contribution. Further analysis of the larger anomalous events suggests that the state of Atlantic and Pacific oceans can govern the occurrence of those events, and the conditions of Atlantic Ocean can be considered a potential modulator of anomalous phenomena of precipitation in ENEB.

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The high variability in the hydrological regime of the Eastern Hydrological Region (EHR) of Northeast Brazil often results in floods and droughts, leading to serious socioeconomic issues. Therefore, this work aimed to investigate connections between spatiotemporal hydrological variability of the EHR and large-scale climate phenomena. Multivariate statistical techniques were applied to relate climate indices with hydrological variables within two representative river basins in the EHR. The results indicated a multi-annual relationship between the state of the sea surface temperature of the Atlantic and Pacific oceans and anomalous hydrological variability in the basins. In addition, the northern Tropical Atlantic conditions were shown to play an important role in modulating the long-term variability of the hydrological response of the basins, whilst only extreme ENSO anomalies seemed to affect the rainy season. This knowledge is an important step towards long-term prediction of hydrological conditions and contributes to the improvement of water resources planning and management in the EHR.