Predictability of the extreme precipitation days in central Eastern Africa during january to may period

Extreme precipitation events significantly affect both human activities and the natural environment. Despite their severe impacts, accurately predicting these extreme precipitation events remains a substantial challenge.The study explores the predictability of extreme precipitation events in Central Eastern Africa (CEA) from January to May, aiming to improve forecasting and provide insights for policymakers and meteorological agencies. The study uses data from Rwanda, Burundi, Uganda, Kenya, and Tanzania and employs a physics-based empirical (P-E) model to identify relevant predictors for extreme precipitation events. The research uses observatin data from Climate prediction center (CPC), Monthly mean sea level pressure (MSLP) Temperature at 2 m, geopotential height and winds, and employs a physics-based empirical (P-E) model to identify relevant potential predictors and precursors for extreme precipitation events. The findings indicated that the P-E models show improved predictability of extreme precipitation days over CEA, with positive correlations for Central East Africa Predictor (EAP1 &3) for Mean sea level pressure and Temperature at 2 m height, but negative correlation for EAP2 for sea serfuce temperature. EPD3 is associated with the temperature at 2-meter height, where positive temperature values result in positive EPDs across the entire region. The percentile-based extreme precipitation index, which is consistent with earlier studies, takes regional variances into account. If the daily precipitation exceeds the 90th percentile of all rainfall records (daily rainfall > 0.1 mm) for the entire 40 years (1981–2020), it is considered an extreme precipitation event on this study. Utilizing the identified predictors, a series of Physics-based Empirical models is developed. These correlations serve as a robust indication of the temporal predictability for Extreme Precipitation Days (EPDs) during the March-April-May (MAM) season over Central East Africa (CEA). The research emphasizes the influence of equatorial Indo-Pacific Sea Surface Temperature (SST) anomalies on Extreme Precipitation Days (EPDs) in Central East Africa (CEA). It elucidates the complex interplay between large-scale anomalies related to regional EPD indices, particularly during El Niño. The study highlights that the prolonged presence of the anticyclonic anomaly, combined with a positive air-sea feedback mechanism, amplifies the likelihood of EPDs in CEA. This heightened probability is attributed to atmospheric changes induced by El Niño and the associated SST anomalies, fostering conditions conducive to increased precipitation events in the region.