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Precipitation nowcasting is an important task in the fields of transportation, traffic, agriculture, and tourism. One of the main challenges is radar echo maps forecasting. It is regarded as a spatiotemporal sequence prediction problem. The prevailing approaches including the state-of-the-art methods are all based on the ConvRNN which combines the Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). However, the feature flow delivered in multi-layer CNNs and RNN usually accompanies the information loss. Therefore, these algorithms fail to model the long-term dependency and the heavy rainfalls tend to be underestimated. In addition, they cannot predict the increasing intensity trend of heavy rainfalls. In this paper, we propose a PredRANN model by embedding the Temporal Attention Module (TAM) and Layer Attention Module (LAM) into the prediction unit to preserve more representation …