This paper introduces a Convolutional LSTM (ConvLSTM) network for precipitation nowcasting, which is formulated as a spatiotemporal sequence forecasting problem. The ConvLSTM extends the fully connected LSTM (FC-LSTM) by incorporating convolutional structures in both input-to-state and state-to-state transitions, enabling better modeling of spatiotemporal correlations. The proposed model is end-to-end trainable and outperforms FC-LSTM and the state-of-the-art operational ROVER algorithm in precipitation nowcasting. The ConvLSTM network is evaluated on synthetic Moving-MNIST dataset and a new real-life radar echo dataset, showing consistent superiority in capturing spatiotemporal patterns. The model's performance is further validated using several precipitation nowcasting metrics, including rainfall mean squared error, critical success index, false alarm rate, probability of detection, and correlation. The results demonstrate that ConvLSTM outperforms FC-LSTM and ROVER in terms of accuracy and generalization, especially in handling boundary conditions and complex spatiotemporal patterns. The ConvLSTM network is also shown to be effective in capturing long-term dependencies and is suitable for spatiotemporal data due to its inherent convolutional structure. The paper concludes that the ConvLSTM model provides a promising approach for precipitation nowcasting and suggests future work on applying ConvLSTM to video-based action recognition.This paper introduces a Convolutional LSTM (ConvLSTM) network for precipitation nowcasting, which is formulated as a spatiotemporal sequence forecasting problem. The ConvLSTM extends the fully connected LSTM (FC-LSTM) by incorporating convolutional structures in both input-to-state and state-to-state transitions, enabling better modeling of spatiotemporal correlations. The proposed model is end-to-end trainable and outperforms FC-LSTM and the state-of-the-art operational ROVER algorithm in precipitation nowcasting. The ConvLSTM network is evaluated on synthetic Moving-MNIST dataset and a new real-life radar echo dataset, showing consistent superiority in capturing spatiotemporal patterns. The model's performance is further validated using several precipitation nowcasting metrics, including rainfall mean squared error, critical success index, false alarm rate, probability of detection, and correlation. The results demonstrate that ConvLSTM outperforms FC-LSTM and ROVER in terms of accuracy and generalization, especially in handling boundary conditions and complex spatiotemporal patterns. The ConvLSTM network is also shown to be effective in capturing long-term dependencies and is suitable for spatiotemporal data due to its inherent convolutional structure. The paper concludes that the ConvLSTM model provides a promising approach for precipitation nowcasting and suggests future work on applying ConvLSTM to video-based action recognition.