Abstract: The present invention discloses an El Nino extreme weather warning method based on incremental learning, comprising: through supervised representation learning, selectively constraining, by a multi-scale feature frequency domain distillation technology, drift of low-frequency components of the multi-scale features based on incremental training, and memorizing knowledge learned by the parallel convolutional neural networks in old tasks; adaptively learning different fusion parameters according to different time spans of the input multi-scale data by using a multi-scale feature adaptive fusion technology, so as to enhance the ability to learn new tasks; and outputting a Nino3.4 index reflecting a change rule of El Nino through fully connected layers according to the adaptively fused features, establishing a mapping function of an extreme rainfall probability r based on the Nino3.4 index, and in response to predicting that the value r goes beyond a threshold value k.