Wavelet Coherence Between Large-Scale Climate Indices and Precipitation Variability over Nigeria (1961–2021)

Abstract

Precipitation variability across Nigeria is strongly modulated by remote climate teleconnections, yet most existing assessments examine individual indices in isolation or rely on trend statistics that cannot resolve how index–rainfall relationships evolve across time scales. This study applies wavelet coherence analysis to characterize the time–frequency relationship between five climate indices the Atlantic Meridional Mode (AMM), Atlantic Multidecadal Oscillation (AMO), North Atlantic Oscillation (NAO), Niño 3.4 index (denoted NINA), and Southern Oscillation Index (SOI) and monthly precipitation across five climatic zones of Nigeria (Sahel, Sudan savannah, Guinea savannah, tropical rainforest, and coastal) over the 60-year period 1961–2021. Monthly precipitation was extracted from the ERA5 reanalysis at twenty-five locations grouped into the five zones, and index data were obtained from NOAA and the Climate Prediction Center. Wavelet coherence and cross-wavelet phase analysis were used to identify coherent time–frequency bands, and kriging interpolation was used to map the spatial structure of precipitation–index correlation across the country. The AMM showed the strongest and most consistent coherence with precipitation across all five zones, concentrated near an approximately annual periodicity, identifying it as the dominant large-scale driver of Nigerian rainfall variability among the indices considered. The AMO and NAO exhibited more intermittent, regionally and seasonally dependent coherence; the Niño 3.4 index showed its clearest influence in the tropical rainforest zone; and the PDO and SOI displayed comparatively weak and inconsistent associations. Spatial correlation mapping showed the negative influence of the NAO and AMO on rainfall strengthening toward the southern coastline and weakening inland, whereas the Niño 3.4 index displayed a south–north gradient of increasingly negative correlation. These findings identify the AMM as a priority predictor for seasonal rainfall forecasting in Nigeria and provide zone-specific evidence to guide climate adaptation planning in agriculture and water resource management.