Understanding the major drivers of Ethiopian JJAS rainfall variability is crucial for monitoring climate extremes such as drought and flood events, which have serious implications for lives, livelihoods and food security. This study investigates the atmospheric and oceanic mechanisms that modulate JJAS rainfall using composite analysis, probability evaluation of the Z-index, and correlation analysis with leading climate drivers, including sea surface temperatures (SSTs), wind circulation, and outgoing longwave radiation (OLR). The results show that 40.3% of JJAS rainfall is normal, 29.5% and 30.2% are dry and wet, respectively. Wet years have sharply increased since 1998, showing a shift in the rainfall patterns. Wind circulation analysis shows that 850 hPa westerly and 200 hPa easterly winds occur during wet years, which enhance the transport of moisture and convection, whereas dry years have their wind patterns in reverse, suppressing rainfall. The correlation of Sea Surface Temperature with rainfall in JJAS has a very significant negative correlation (？0.8) in central and eastern Pacific SSTs, indicating La Ni？a enhancing rainfall and El Ni？o deficit it. Conversely, a significant positive correlation (0.8) in the western Pacific modulating the regional SST anomaly Ethiopian rainfall. The Nino 3.4 Index shows a significant negative relationship (？0.5 to ？0.8) with Ethiopia’s JJAS rain, especially in the northeast, central, and eastern regions, the key role of the ENSO in rainfall variability. Moreover, the negative OLR anomaly and high RH, promote cloudiness and precipitation, while dry years are distinguished by the higher OLR anomaly and reduced RH, which suppress convection. These results confirm the leading influence of the El Nino-Southern Oscillation (ENSO) in controlling Ethiopian rainfall variability and suggest that monitoring of SST structure, particularly the Nino 3.4 Index, might enhance seasonal rainfall prediction and inform the Ethiopian climatic change strategy. Future studies should incorporate high-resolution modeling, improved observations, advanced statistics, and Machine Learning to better comprehend Ethiopia’s climate extremes.
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