## 2 Data and Methods  
### Data  
A total of 236 TCs from 2015 to 2022, which are obtained from the China Meteorological Administration's (CMA) TC best track dataset, are employed in this study [38]. The best track dataset provides the longitude and latitude of the TC center, the minimum central pressure, and the maximum sustained wind speed around the TC center, with a time interval of 3 h or 6 h (Since 2017, for TC making landfall in China, the time-frequency of the best track has been increased to every 3 h within the 24 h before its landfall). There are altogether 2084 TC point samples for these 236 TCs in the Northwest Pacific region (100-130 E, 10-40 N). These samples are classified as tropical depression (TD), tropical storm (TS), severe tropical storm (STS), typhoon (TY), severe typhoon (STY), and super typhoon (SuTY) according to their real-time intensities. Due to the relatively small number of TC point samples for SuTY and STY compared with the other TC categories, these samples are combined and referred to as SSTYs in this study [39]. A total of 1844 TC point samples from 2015 to 2021 are selected for training the neural network model, and 240 TC point samples in 2022 are selected for model testing. Table 1 shows the quantities of TC point samples for different intensities, while Figure 1 depicts the locations of these TC point samples, with different colors to denote their varying intensities. This study uses cumulative precipitation with lead times of 6, 12, 18, 24, 30, 36, 42, 48, 54, 60, 66, and 72 h within a 20 \(\times\) 20 degree region around the TC point locations as the predictands, which is provided by the Global Precipitation Measurement (GPM) Mission with a resolution of 0.25 degrees [40]. The predictors include real-time environmental background data and cumulative precipitation forecast data with the lead times within the 20 \(\times\) 20 degree region around the TC point locations, which is provided by GFS [41]. Predictive modeling in this study is implemented using UNet-based neural networks. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Intensity** & **Test Set** & **Training Set** \\ \hline TD & 73 & 651 \\ TS & 56 & 545 \\ STS & 31 & 232 \\ TY & 34 & 151 \\ SSTY & 46 & 265 \\ all & 240 & 1844 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantities of TC point samples for each intensity level. GFS is an atmospheric circulation model developed and maintained by the National Oceanic and Atmospheric Administration (NOAA) of the United States. The GFS data have a spatial resolution of 0.25 degrees and a temporal resolution of 6 h. The TC point samples are selected from the best track dataset at 00, 06, 12, and 18 UTC to align with the daily GFS model forecast runs. To forecast the cumulative precipitation for a given time frame, the following steps are taken to process the GFS data for creating the input field of neural networks. (1) Calculate the coordinate range of 10 latitudes and longitudes around the TC point based on its coordinates and time to select a 20 \(\times\) 20 degree region whose center is the TC point location. (2) Select cumulative precipitation forecast data corresponding to the specific lead time, and real-time environmental field data consisting of 24 atmospheric factors, based on the coordinate range. Table 2 displays the names and physical meanings of the cumulative precipitation forecast data and real-time environmental field data of 24 atmospheric factors, each with a data size of 80 \(\times\) 80 (20 degree/0.25 degree resolution). (3) Integrate the data into a 25 \(\times\) 80 \(\times\) 80 array, constituting the neural network's input field for a TC point with a specific lead time. Each input field for a TC point includes cumulative precipitation forecast data with a size of 80 \(\times\) 80 and real-time environmental field data with a size of 24 \(\times\) 80 \(\times\) 80. Figure 1: TC point sample positions are denoted by “\(\times\)” for model training and “+” for model testing.