Wavelet empirical orthogonal functions and/or Hilbert transformations are applied to the spatial, temporal and frequency regimes of seasonal climate variability, that are related to the future variability of streamflow or runoff at weekly time scales through a genetic algorithm neural network-disaggregation model, without modeling the complex rainfall-runoff process. This scheme is shown to preserve critical statistical properties of the future weekly streamflow, vital for reservoir operations. Experimental results from the Kafue River basin (95, 000 km2), in Zambia, Central Southern Africa shows encouraging results.