The El Niño-Southern Oscillation(ENSO)is an anomaly of air-sea interaction on an interannual time scale in the tropical Pacific Ocean,and its occurrence is characterized by the Niño3.4 index.In addition,ENSO is closely related to many extreme climatic events.Therefore,effective ENSO prediction is of great significance for preventing extreme climate events and in-depth study of global climate change.However,most ENSO predictions based on deep learning predict an index or a single variable,and there are few research on the space-time evolution of ENSO under the simulation of multi-climate factors.MEPM,a multivariate ENSO prediction model,was presented.These include multivariate information extraction module(MIEM)and spatial-temporal fusion module(STFM),to capture the interdependencies of different climate elements in time and space,thereby improving the accuracy of ENSO prediction.The anomalies of latitude wind stress anomaly(τx),longitude wind stress anomaly(τy),sea surface temperature anomaly(SSTA)and 150 m below sea surface temperature anomaly(SSTA150)were selected,and sufficient experiments were carried out.The results show that MEPM is 10％,20％and 14％higher,respectively,than dynamic forecasting systems CanCM4,CCSM3,and GFDL-aer04 in North American multi-model ensemble on the average of Niño3.4 index-related techniques 11 months in advance.In addition,MEPM significantly outperforms other deep learning models on Nino3.4 index-related techniques over the medium term and provides valid predictions up to 17 months.
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