Spatiotemporal Variability and Trends of Rainfall in Zambia and their Links to Oceanic Teleconnections
Clara Liapapa
School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing, China and Zambia Meteorological Department, Lusaka, Zambia.
Gerverse Kamukama Ebaju *
Jiangsu Key Laboratory of Agricultural Meteorology, School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing, China.
Thadee Niyigena
School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing, China.
Martha Adongo Obuo
School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing, China.
*Author to whom correspondence should be addressed.
Abstract
Rainfall variability strongly influences agriculture, water resources, and socio-economic stability in Zambia, where livelihoods largely depend on seasonal precipitation. Understanding the spatial and temporal dynamics of rainfall is therefore essential for effective climate risk management. This study investigates the spatiotemporal variability of rainfall across Zambia during 1993–2024 and examines its relationship with large-scale ocean–atmosphere interactions. Monthly precipitation data from the ERA5 reanalysis and sea surface temperature (SST) fields from NOAA were analyzed using standardized rainfall anomalies, the Mann–Kendall trend test, Sen’s slope estimator, Empirical Orthogonal Function (EOF) analysis, and correlation analysis. Results indicate that Zambia exhibits a unimodal rainfall regime, with most precipitation occurring during the NDJFMA rainy season (November–April). Trend analysis reveals spatially heterogeneous rainfall changes, with statistically significant increases in northern and northeastern Zambia, reaching approximately 8 mm yr⁻¹, while central and southern regions display weak or non-significant trends. Temporal analysis highlights strong interannual variability, with rainfall fluctuations largely controlled by variations during the main rainy season. EOF analysis shows that the leading mode (EOF1) explains 53% of total rainfall variance, representing a coherent countrywide rainfall pattern, while the second mode (EOF2) accounts for 15.3% of the variance and reflects a north–south rainfall dipole. Correlation analysis further demonstrates that Zambia’s rainfall variability is significantly linked to SST anomalies in the tropical Pacific and Indian Oceans. In particular, El Niño conditions are generally associated with below-normal rainfall, whereas La Niña conditions favor wetter-than-normal seasons. Overall, the findings highlight the dominant influence of large-scale ocean–atmosphere interactions on Zambia’s rainfall variability and provide useful insights for improving seasonal climate prediction, water resource management, and agricultural planning under a changing climate.
Keywords: Rainfall variability, empirical orthogonal function, Zambia and ERA5 reanalysis