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Abstract. Interdecadal cycles in rainfall influence long-term hydrological variability, affecting water resource management, agriculture, and flood or drought preparedness across the globe. Previous studies have found evidence of cycles over limited regions but the global distribution and relationship to the major climate modes remain unclear. Using the global GPCC v2022 2.5° gridded dataset (1891–2020), we applied a Gaussian mixture model to detect significant clustering of cycles in rainfall, derived from wavelet analysis of individual grid points. Three Global Rainfall Cycles (GRCs) emerged at 12.9, 19.9, and 28.2 years, were widespread, and aligned in length and phase to previous research. Two longer cycles (35.9 and 45.9 years) were also significant but interpreted cautiously due to their period relative to the dataset's length. The 12.9 and 19.9 year GRCs showed strong phase coherence and spatial overlap with the El Niño-Southern Oscillation and Interdecadal Pacific Oscillation climate modes, but not with the Indian Ocean Dipole or North Atlantic Oscillation. Notably, GRCs explained more rainfall variance than expected from the effect of these climate modes alone, suggesting another driver may influence rainfall directly and via climate interactions. These findings are of significance to global water management and rainfall modelling, offering the potential to enhance flood and drought forecasting in strongly affected regions.
Selkirk et al. (Fri,) studied this question.