In recent years, artificial intelligence has been utilized to optimize quantitative precipitation forecasts, with deep learning (DL) approaches being the most commonly used due to their remarkable capacity to mimic nonlinear systems [12]. DL has been widely employed to improve numerical precipitation forecasts by effectively understanding and reconstructing geophysical fields [13,14]. For example, training the DL networks of physical parameterization schemes, such as convective parameterization and cloud microphysical parameterization, might enhance model precipitation projections [13,14]. DL can also be used in conjunction with the NWP model for bias correction. [PERSON] et al. (2023) proposed a data augmentation U-Net with batch normalization (DABU-Net) for improving winter precipitation forecasting in Southeastern China [15]. Similarly, [PERSON] et al. (2021) proposed a deep learning-based data-driven bias correction method for Yin-He global spectral model (YHGSM) reforecasting [16]. The U-Net-based model can reduce the root mean square error (RMSE) and improve the threat score (TS), particularly for heavy precipitation. DL is also frequently employed in TC forecasting [17], however, the primary study topics are track and intensity forecasting [18]. [PERSON] et al. (2023) used a DL prediction model to make clever seasonal predictions of TC track density in the Northwest Pacific region during the typhoon season, and the results showed that the TC track distributions were predicted better than the best weather prediction models [19]. [PERSON] et al. (2017) suggested a deep convolutional neural network (CNN) for TC intensity classification based on a long time series of TC infrared cloud images. The model achieved an RMSE of 10.18 kt for intensity estimation [20]. Although DL has been widely used in precipitation and TC forecasting, little research has explored its application in improving TC precipitation forecasting [21,22]. Among the DL models used in precipitation forecasting, the U-Net model has achieved significant success [23,24,25]. U-Net, a CNN-based network, can simulate nonlinear functions flexibly and efficiently [26,27]. [PERSON] et al. (2019) compared various methods for calculating total precipitation and found that U-Net significantly outperformed other models, such as random forests, visual geometry group (VGG)-16, and segmentation network (SegNet) [28]. Similarly, [PERSON] et al. (2020) discovered that U-Net surpassed other classical machine-learning approaches, such as random forests and logistic quantile regression [29]. [PERSON] et al. (2021) demonstrated that a U-Net model based on residual learning can reveal the physical relationships that lead to target precipitation [30]. U-Net's performance can be improved by modifying the neural network structures [31,32,33]. One potential enhancement strategy is the use of a squeeze-and-excitation U-Net (SE-UNet), which combines SE-Net with U-Net [31,32,33]. The squeeze-and-excite modules activate helpful features and deactivate useless features in an adaptively weighted manner, resulting in a significant improvement in network capacity with only a few additional parameters and memory costs [34]. Another enhancement, UNet3+, employs full-scaleskip connections to combine low-level detail with high-level semantics from feature maps at different scales, maximizing the use of full-scale feature maps. This approach has demonstrated promise in weather monitoring applications [35; 36; 37]. Despite their potential, there have been few studies conducted on the application of SE-UNet and UNet3+ in improving precipitation forecasting. In this study, we employ deep learning models, including U-Net, UNet3+, SE-Net, and SE-UNet3+, to forecast TC precipitation in the Northwest Pacific region, a region known for its frequent and impactful TC activities. These models are based on the U-Net structure and are trained using Global Forecast System (GFS) precipitation forecast data and real-time GFS data.