Abstract: Radar echo extrapolation is one of the main reference criteria for nowcasting, weather modification operation and evaluation of its effectiveness. Therefore, rapid and accurate echo extrapolation technology is always a research hotspot in the field of radar meteorology. In recent years, deep learning-based spatiotemporal sequence prediction models have been widely applied in radar echo extrapolation. However, most of the inputs to these extrapolation networks are only grayscale images converted from the radar echo intensity products shown with 16-level color code pseudo colors, and thus some echo details are lost. The error inevitably increases with the extrapolation time. The initiation, disappearance, movement, and evolution of echoes are closely related to weather background. Based on this consideration, some physical products in the initial zero field of the rapid-refresh multi-scale analysis and prediction system-nowcasting (RMAPS-NOW) developed by the Institute of Urban Meteorology, CMA, are integrated with the raw data of the North China radar mosaic to construct multiple radar cells. Based on convolutional long short-term memory (ConvLSTM) network, a multi-channel radar echo extrapolation (MR-ConvLSTM) network is designed by using the radar cells as inputs. In addition, considering the smoothness of the convolutional algorithm, a self-defined loss function is designed to increase the spatiotemporal weight of the echo intensity for spatiotemporal attenuation correction. A total of 13000 samples of radar mosaic and RMAPS-NOW data during June—September from 2018 to 2021 over the area of (40.65°—41.65°N, 114°—115.4°E) are selected. 80% of the samples, i.e., 10400 samples, are used as the training data; and 20% of the samples, i.e. 2600, are used as the test data. The introduced physical products include u, v wind (1350 m), relative humidity (150 m), and horizontal divergence (1350 m), etc. at multiple altitude levels. Based on ConvLSTM and MR-ConvLSTM with the self-defined loss function, 5 extrapolation models are then trained respectively. Using critical success index (CSI), hit rate (POD), and false alarm rate (FAR) as evaluation indexes, the models are evaluated by the test data. At the values of reflectivity threshold 20, 30, and 35 dBz, the average values of CSI calculated by the MR-ConvLSTM-based and the self-defined function models with integrated physical products are 4.67%, 13.8%, 5.98% higher and the average values of POD are 3.1%, 7.68%, 8.38% higher, while average values of FAR are 6.37%, 8.54%, 10.17% lower than those by the ConvLSTM-based model without integrated physical products, respectively. The model with three physical products introduced (RH, u-wind, v-wind) performs the best for all the indexes, and average CSI and POD are respectively 16.01% and 13.38% higher while FAR is 14.88% lower than those without physical products. From the visualization cases of model application, it can also be seen that the introduction of physical quantities effectively improves the accuracy of radar echo extrapolation. These results indicate that the MR-ConvLSTM models and self-defined loss function have robust generalization ability.