Abstract: A deep learning-based precipitation nowcast model (PhyDNet-RP) over Jiangsu area is established using a physically-constrained convolutional neural network PhyDNet with both radar reflectivity and ground precipitation observations as inputs. PhyDNet-RP is then compared with INCA (blend TREC with mesoscale numerical model), PhyDNet-P (only precipitation is used as input) and UNet-RP (both precipitation and radar data are used as input for convolutional neural network UNet), especially under convection enhancement scenarios. Results are as follows: (1) The deep learning-based nowcast model performs better than INCA on the forecast of precipitation growth and decay. (2) The comparison of PhyDNet-P and PhyDNet-RP reveals that adding radar reflectivity as a second input factor can improve the forecast of movement and form of strong precipitation. (3) UNet-RP can to some extent reflect the movement and form of precipitation, but it fails to capture the intensity. It is concluded that PhyDNet-RP shows an overall advantage over INCA, PhyDNet-P, and UNet-RP, indicating that precipitation nowcast obviously benefits from employing different factors as input for deep learning model.