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Managing water resources in the face of climate uncertainties is a critical challenge that requires innovative approaches and accurate forecasting techniques. This study focuses on the development of a novel methodology for predicting rainfall anomalies over interannual to decadal time scales, with the aim of enhancing water supply management. The proposed approach utilizes the Wavelet System Prediction (WASP) methodology to transform climate indices and improve the accuracy of rainfall forecasting. Furthermore, the forecast skill is enhanced through the Hierarchical Linear Combination (HLC) method (Choudhury et al., 2019), which harmonizes forecasts derived from ten climate indices. These indices, including Niño 3, Niño 4, Niño 3.4, the El Niño Modoki index (EMI), the Dipole Mode Index (DMI), the Indian Ocean East Pole Index (EPI), the Indian Ocean West Pole Index (WPI), the Indonesian Index (II), the Tasman Sea Index (TSI) and the Tropical Trans-basin Variability Index (TBV), are derived from decadal predictions of the Decadal Climate Prediction Project (DCPP) of the Phase 6 of Coupled Model Intercomparison Project (CMIP6) model. The results unveil the potential of the hybrid HLC-WASP framework to significantly improve the forecasting of medium to long-term rainfall anomalies, enabling water managers to adapt their plans and optimize reservoir usage accordingly. By providing more accurate predictions of rainfall anomalies, this research contributes to effective water resource management and aids in decision-making processes for water allocation and conservation. [ABSTRACT FROM AUTHOR]