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.

A new database of the Entomological Inoculation Rate (EIR) is used to directly link the risk of infectious mosquito bites to climate in Sub-Saharan Africa. Applying a statistical mixed model framework to high-quality monthly EIR measurements collected from field campaigns in Sub-Saharan Africa, we analyzed the impact of rainfall and temperature seasonality on EIR seasonality and determined important climate drivers of malaria seasonality across varied climate settings in the region. We observed that seasonal malaria transmission requires a temperature window of 15-40 degrees Celsius and is sustained if average temperature is well above the minimum or below the maximum temperature threshold. Our study also observed that monthly maximum rainfall for seasonal malaria transmission should not exceed 600 mm in west Central Africa, and 400 mm in the Sahel, Guinea Savannah and East Africa. Based on a multi-regression model approach, rainfall and temperature seasonality were significantly associated with malaria seasonality in most parts of Sub-Saharan Africa except in west Central Africa. However, areas characterized by significant elevations such as East Africa, topography has a significant influence on which climate variable is an important determinant of malaria seasonality. Malaria seasonality lags behind rainfall seasonality only at markedly seasonal rainfall areas such as Sahel and East Africa; elsewhere, malaria transmission is year-round. The study’s outcome is important for the improvement and validation of weather-driven dynamical malaria models that directly simulate EIR. It can contribute to the development of malaria models fit-for-purpose to support health decision-making towards malaria control or elimination in Sub-Saharan Africa.

Short-term global ensemble predictions of rainfall currently have no skill over northern tropical Africa when compared to simple climatology-based forecasts, even after sophisticated statistical postprocessing. Here we demonstrate that statistical forecasts for the probability of precipitation based on a simple logistic regression model have considerable potential for improvement. The new approach we present here relies on gridded rainfall estimates from the Tropical Rainfall Measuring Mission for July--September 1998--2017 and uses rainfall amounts from the pixels that show highest positive and negative correlations on the previous two days as input. Forecasts using this model are reliable and have a higher resolution and better skill than climatology-based forecasts. The good performance is related to westward propagating African easterly waves and embedded mesoscale convective systems. The statistical model is outmatched by the postprocessed dynamical forecast in the dry outer tropics only, where extratropical influences are important.