## 4 Conclusions  
In this study, we employ UNet-based DL methods, including U-Net, UNet3+, SE-Net, and SE-UNet3+, to forecast TC precipitation in the Northwest Pacific region based on GFS precipitation forecast data and real-time GFS environmental background data. To maximize the utilization of the precipitation forecasts from these four DL models, we additionally employ two ensemble methods, PM and AVR, for rainfall prediction. GPM precipitation data are applied as the ground truth. Compared with the GFS model, the RMSEs of UNet, UNet3+, SE-UNet, SE-UNet3+, AVR, and PM are lowered by 8.7%, 10.1%, 9.7%, 10.0%, 11.4%, and 11.5%, respectively, while the MAEs are reduced by 9.6%, 11.3%, 9.0%, 12.0%, 12.8%, and 13.0%. The TSs of the precipitation forecasts generated by these six models are higher than those of the GFS precipitation forecast, indicating that UNet-based models outperform the GFS model. Notably, the ensemble models, particularly PM, outperform all other individual models. By comparing the spatial distribution of RMSE and MAE for precipitation prediction by PM and GFS models, we find that the PM model can effectively improve the precipitation forecast accuracy near the TC center, where the GFS encounters significant precipitation forecast errors. Furthermore, the feature importance analysis of U-Net, UNet3+, SE-UNet, and SE-UNet3+ reveals that the GFS precipitation forecast data are the most critical variable among the 25 input variables, followed by the 1 sm. Additionally, latitudinal wind speed at 500 hPa and vertical wind speed at 500 hPa are also important. The study's innovation lies in adopting the UNet3+ architecture, rarely explored in weather prediction research, and pioneering the application of probability matching techniques in TC precipitation forecasting. This hybrid approach, combining traditional numerical forecasting integration with DL methods, represents a significant advancement in TC precipitation prediction, offering novel insights and promising ways for future weather forecasting. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/w16050671/s1](https://www.mdpi.com/article/10.3390/w16050671/s1)]([https://www.mdpi.com/article/10.3390/w16050671/s1](https://www.mdpi.com/article/10.3390/w16050671/s1)), Figure S1: VGG model structure; Figure S2: Boxplots of MAE for different models with various lead times; Figure S3: MAEs of different models for various TC levels; Figure S4: The spatial distribution of precipitation prediction MAE (mm) by PM and GFS models with different lead times; Figure S5: The first 10 significant features for 48-h accumulated precipitation prediction; Figure S6: The first 10 significant features for 72-h accumulated precipitation prediction. Figure 10: The first 10 significant features for 24 h accumulated precipitation prediction by the models of (**a**) U-Net, (**b**) SE-UNet, (**c**) UNet3+, and (**d**) SE-UNet3+. **Author Contributions:** Conceptualization, L.H. and Q.L. ; Data curation, L.H. ; Formal analysis, L.H. and Q.L. ; Funding acquisition, Q.L. ; Investigation, L.H. and Q.L. ; Methodology, L.H., Q.L. and J.Z. ; Project administration, Q.L. ; Resources, Q.L., L.H. and J.Z. ; Software, L.H. and Q.L. ; Supervision, Q.L. ; Validation, L.H., Q.L. and J.Z. ; Visualization, L.H. ; Writing--original draft, L.H. and Q.L. ; Writing--review and editing, Q.L., X.D., Z.W., Y.W., P.-W.C. and N.L. All authors have read and agreed to the published version of the manuscript. **Funding:** This study was supported by the Shenzhen Municipal Committee of Science and Technology Innovation with Grants of JCYJ20210324101006016 and GJHZ20210705141403010. **Data Availability Statement:** The CMA tropical cyclone best track dataset is available at [[https://tcdata.typhoon.org.cn/zjjjj.html](https://tcdata.typhoon.org.cn/zjjjj.html)]([https://tcdata.typhoon.org.cn/zjjjj.html](https://tcdata.typhoon.org.cn/zjjjj.html)) (accessed on 2 July 2023).