The decline of the eastern East African (EA) March-April-May (MAM) rains poses a life-threatening “enigma,” an enigma linked to sequential droughts in the most food-insecure region of the world. The MAM 2022 drought was the driest on record, preceded by three poor rainy seasons, and followed by widespread starvation. Connecting these droughts is an interaction between La Niña and climate change, an interaction that provides exciting opportunities for long-lead prediction and proactive disaster risk management. Using observations, reanalyses, and climate change simulations, we show here, for the first time, that post-1997 OND La Niña events are robust precursors of: (1) strong MAM “Western V Gradients” in the Pacific, which help produce (2) large increases in moisture convergence and atmospheric heating near Indonesia, which appear associated with (3) regional shifts in moisture transports and vertical velocities, which (4) help explain more frequent dry EA rainy seasons. Understanding this causal chain will help make long-lead forecasts more actionable. Increased Warm Pool atmospheric heating and moisture convergence sets the stage for dangerous sequential droughts in EA. At 20-year time scales, we show that these Warm Pool heating increases are attributable to observed Western V warming, which is, in turn, largely attributable to climate change. As energy builds up in the oceans and atmosphere, we see stronger convergence patterns, which offer opportunities for prediction. Hence, linking EA drying to a stronger Walker Circulation can help explain the “enigma” while underscoring the predictable risks associated with recent La Niña events.

This commentary discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision-making. Following an unprecedented sequence of five droughts, in 2022 23 million east Africans faced starvation, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution-based insights can be combined with the latest dynamic models to predict droughts at eight-month lead-times. We then discuss behavioral and social barriers to forecast use, and review literature examining how EWS might (or might not) enhance agro-pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization (WMO) “Early Warning for All” plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limited funding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African-led EWS, and building better links between EWS and agricultural development efforts can support long-term adaptation, reducing chronic needs for billions of dollars in reactive assistance. The main messages of this commentary will be widely. Climate change is interacting with La Niña to produce extreme, but extremely predictable, Pacific sea surface temperature gradients. These gradients will affect the climate in many countries creating opportunities for prediction. Effective use of such predictions, however, will demand cross-silo collaboration.

In operational analyses of the surface moisture imbalance that defines drought, the supply aspect has generally been well characterized by precipitation; however, the same count be said of the demand side—a function of evaporative demand (E0) and surface moisture availability. In drought monitoring, E0 is often poorly parameterized by a climatological mean, by non-physically based estimates, or is neglected entirely. One problem has been a paucity of driver data—on temperature, humidity, solar radiation, and wind speed—required to fully characterize E0. This deficient E0 modeling is particularly troublesome over data-sparse regions that are also home to drought-vulnerable populations, such as across much of Africa. There is thus urgent need for global E0 estimates for physically accurate drought analyses and food security assessments; further we need an improved understanding of how E0 and drought interact and to exploit these interactions in drought monitoring. In this presentation we explore ways to meet these needs. From MERRA-2—an accurate, fine-resolution land-surface/atmosphere reanalysis—we have developed a >38-year, daily, global Penman-Monteith reference ET dataset as a fully physical metric of E0. This dataset is valuable for examining hydroclimatic changes and extremes. A novel drought index based on this dataset—the Evaporative Demand Drought Index (EDDI)—represents drought’s demand perspective, and permits early warning and ongoing monitoring of agricultural flash drought and hydrologic drought. We highlight the findings of our examination of E0-drought interactions and using EDDI in Africa. Using reference ET as an E0 metric has permitted explicit attribution of the variability of E0 across Africa, and of E0 anomalies associated with canonical droughts in the Sahel region. This analysis determines where, when, and to what relative degree each of the individual drivers of E0 affects the demand side of drought. Using independent estimates of drought across space and time—CHIRPS precipitation and the Normalized Difference Vegetation Index for 1982-2015—we examine the differences between drought and non-drought periods, and between precipitation-forced droughts and droughts forced by a combination of precipitation and E0.