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Background: Ethiopia has a history of climate related malaria epidemics. An improved understanding of malaria–climate interactions is needed to inform malaria control and national adaptation plans. Methods: Malaria–climate associations in Ethiopia were assessed using (a) monthly climate data (1981–2016) from the Ethiopian National Meteorological Agency (NMA), (b) sea surface temperatures (SSTs) from the eastern Pacific, Indian Ocean and Tropical Atlantic and (c) historical malaria epidemic information obtained from the literature. Data analysed spanned 1950–2016. Individual analyses were undertaken over relevant time periods. The impact of the El Niño Southern Oscillation (ENSO) on seasonal and spatial patterns of rainfall and minimum temperature (Tmin) and maximum temperature (Tmax) was explored using NMA online Maprooms. The relationship of historic malaria epidemics (local or widespread) and concurrent ENSO phases (El Niño, Neutral, La Niña) and climate conditions (including drought) was explored in various ways. The relationships between SSTs (ENSO, Indian Ocean Dipole and Tropical Atlantic), rainfall, Tmin, Tmax and malaria epidemics in Amhara region were also explored. Results: El Niño events are strongly related to higher Tmax across the country, drought in north-west Ethiopia during the July–August–September (JAS) rainy season and unusually heavy rain in the semi-arid south-east during the October–November–December (OND) season. La Niña conditions approximate the reverse. At the national level malaria epidemics mostly occur following the JAS rainy season and widespread epidemics are commonly associated with El Niño events when Tmax is high, and drought is common. In the Amhara region, malaria epidemics were not associated with ENSO, but with warm Tropical Atlantic SSTs and higher rainfall. Conclusion: Malaria–climate relationships in Ethiopia are complex, unravelling them requires good climate and malaria data (as well as data on potential confounders) and an understanding of the regional and local climate system. The development of climate informed early warning systems must, therefore, target a specific region and season when predictability is high and where the climate drivers of malaria are sufficiently well understood. An El Niño event is likely in the coming years. Warming temperatures, political instability in some regions, and declining investments from international donors, implies an increasing risk of climate-related malaria epidemics. [ABSTRACT FROM AUTHOR]

Predictability of Ethiopian Kiremt rainfall (June to September: JJAS) and forecast skill of the European Centre for Medium-Range Weather Forecasts (ECMWF) fifth-generation seasonal forecast system 5 (SEAS5) is explored during 1981–2019. The first empirical orthogonal function of observed rainfall explains 50.6% of the total variability and is characterized by positive rainfall anomalies largely confined over the northwestern and central-western regions of Ethiopia. Consequently, a Kiremt rainfall index (KRI) is defined for this region. The correlation coefficient (CC) between the observed and predicted KRI is 0.68 and 0.53 for May and April starts, respectively. Composite analysis of sea surface temperature (SST) and lower-level circulation based on excess and deficit years of Kiremt rains shows that the El Niño Southern–Oscillation is the main modulator of the Kiremt rainfall variability. The CC between KRI and Niño3.4 index is − 0.62, indicating that El Niño is accompanied by below-normal Kiremt rainfall, while La Niña is accompanied by above-normal amounts. The fifth generation of ECMWF atmospheric reanalysis (ERA5) shows that excess (deficit) Kiremt rainfall anomalies are associated with an anomalous low (high) pressure centered over northeast Arabian Peninsula and an anomalous in-phase (reverse) low-level Somali Jet. SEAS5 reproduces the spatial and temporal components of observed Kiremt rainfall variability, including the main climatic features associated with excess and deficit Kiremt rainfall in May and April starts. However, certain important observed features like above-normal SSTs in the Gulf of Guinea are not well predicted. Results indicate that Kiremt rains has some potential predictability and SEAS5 shows a moderate forecast skill. Probabilistic analysis shows highest values where predictability and deterministic skill are also highest. [ABSTRACT FROM AUTHOR]