The 2012–2018 drought was such an extreme event in the drought-prone area of Northeast Brazil that it triggered a discussion about proactive drought management. This paper aims at understanding the causes and consequences of this event and analyzes its frequency. A consecutive sequence of sea surface temperature anomalies in the Pacific and Atlantic Oceans, at both the decadal and interannual scales, led to this severe and persistent drought. Drought duration and severity were analyzed using run theory at the hydrographic region scale as decision-makers understand impact analysis better at this scale. Copula functions were used to properly model drought joint characteristics as they presented different marginal distributions and an asymmetric behavior. The 2012–2018 drought in Ceará State had the highest mean bivariate return period ever recorded, estimated at 240 years. Considering drought duration and severity simultaneously at the level of the hydrographic regions improves risk assessment. This result advances our understanding of exceptional events. In this sense, the present work proposes the use of this analysis as a tool for proactive drought planning.

The NCEP Regional Spectral Model (RSM), with horizontal resolution of 60 km, was used to downscale the ECHAM4.5 AGCM (T42) simulations forced with observed SSTs over northeast Brazil. An ensemble of 10 runs for the period January–June 1971–2000 was used in this study. The RSM can resolve the spatial patterns of observed seasonal precipitation and capture the interannual variability of observed seasonal precipitation as well. The AGCM bias in displacement of the Atlantic ITCZ is partially corrected in the RSM. The RSM probability distribution function of seasonal precipitation anomalies is in better agreement with observations than that of the driving AGCM. Good potential prediction skills are demonstrated by the RSM in predicting the interannual variability of regional seasonal precipitation. The RSM can also capture the interannual variability of observed precipitation at intraseasonal time scales, such as precipitation intensity distribution and dry spells. A drought index and a flooding index were adopted to indicate the severity of drought and flooding conditions, and their interannual variability was reproduced by the RSM. The overall RSM performance in the downscaled climate of the ECHAM4.5 AGCM is satisfactory over Nordeste. The primary deficiency is a systematic dry bias for precipitation simulation.

The International Research Institute for Climate Prediction (IRI) and Ceará Foundation for Meteorology and Water Resources (FUNCEME) in Brazil have developed a dynamical downscaling prediction system for Northeast Brazil (the Nordeste) and have been issuing seasonal rainfall forecasts since December 2001. To the authors’ knowledge, this is the first operational climate dynamical downscaling prediction system. The ECHAM4.5 AGCM and the NCEP Regional Spectral Model (RSM) are the core of this prediction system. This is a two-tiered prediction system. SST forecasts are produced first, which then serve as the lower boundary condition forcing for the ECHAM4.5 AGCM–NCEP RSM nested system. Hindcasts for January–June 1971–2000 with the nested model, using observed SSTs, provided estimates of model potential predictability and characteristics of the model climatology. During 2002–04, the overall rainfall forecast skill, measured by the ranked probability skill score (RPSS), is positive over a majority of the Nordeste. Higher skill is found for the March–May (MAM) and April–June (AMJ) seasons with forecast lead times up to 3 months. The skill of the downscaled forecasts is generally higher than that of the driving global model forecasts.