  Drought is a costly natural hazard, many aspects of which remain poorly
understood. It has many contributory factors, driving its outset, duration, and
severity, including land surface, anthropogenic activities, and, most
importantly, meteorological anomalies. Prediction plays a crucial role in
drought preparedness and risk mitigation. However, this is a challenging task
at socio-economically critical lead times (1-2 years), because meteorological
anomalies operate at a wide range of temporal and spatial scales. Among them,
past studies have shown a correlation between the Sea Surface Temperature (SST)
anomaly and the amount of precipitation in various locations in Africa. In its
Eastern part, the cooling phase of El Nino-Southern Oscillation (ENSO) and SST
anomaly in the Indian ocean are correlated with the lack of rainfall. Given the
intrinsic shortcomings of correlation coefficients, we investigate the
association among SST modes of variability and the monthly fraction of grid
points in Ethiopia, which are in drought conditions in terms of causality.
Using the empirical extreme quantiles of precipitation distribution as a proxy
for drought, We show that the level of SST second mode of variability in the
prior year influences the occurrence of drought in Ethiopia. The causal link
between these two variables has a negative coefficient that verifies the
conclusion of past studies that rainfall deficiency in the Horn of Africa is
associated with ENSO's cooling phase.
