THE El Nino/Southern Oscillation (ENSO) is a quasi-periodic interannual variation in global atmospheric and oceanic circulation patterns, known to be correlated with variations in the global pattern of rainfall(1-3), Good predictive models for ENSO, if they existed, would allow accurate prediction of global rainfall variations, thus leading to better management of world agricultural production(4,5), as well as improving profits and reducing risks for farmers(6,7). But our current ability to predict ENSO variation is limited, Here we describe a probabilistic rainfall ’forecasting’ system that does not require ENSO predictive ability, but is instead based on the identification of lag-relationships between values of the Southern Oscillation Index, which provides a quantitative measure of the phase of the ENSO cycle, and future rainfall, The system provides rainfall probability distributions three to six months in advance for regions worldwide, and is simple enough to be incorporated into management systems now.