Empirical and numerical studies aiming at predicting inter-annual monsoon variability have thus far shown limited predictive capability. In this study, we develop a spatially explicit seasonal prediction methodology for south-west Asian monsoon (SWM) rainfall in the river basins of the Indus, Brahmaputra and Ganges, using multiple regression linear models in combination with satellite-derived snow cover. We show that the use of recent time series of remotely sensed snow cover, in combination with indices of global ocean and atmospheric modes (ENSO, NAO), can predict average monsoon precipitation with reasonable accuracy and with greater accuracy in specific regions. Maps of the relative contribution of predictor variables to the regression model show that the spring snow cover on the Tibetan plateau is the most important predictor of monsoon precipitation, especially in inland regions.
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