## 2 Near-Term Rainfall Deep Learning Model
### Time-Space Dual-Discriminator Structure  
The structure of the dual temporal and spatial discriminator is illustrated in Figure 1d. Both the spatial and temporal discriminators initially leverage the space-to-depth module, which converts spatial information into the depth (channel) dimension, thereby facilitating a more effective extraction of image features. The spatial discriminator is predominantly composed of inception modules, as depicted in Figure 1f. This module utilizes convolutional kernels of various sizes to enable multi-scale feature extraction from the input data. It excels in processing radar echo images that present complex spatiotemporal characteristics, capturing both large-scale echo movements and small-scale intensity variations within the echoes. In contrast, the temporal discriminator primarily consists of 3D convolution modules. These modules stack consecutive frames from the time series, allowing for the fusion of temporal features and enhancing the temporal discriminator's comprehension of the input data's temporal context. The S2D algorithm performs downsampling of image features by leveraging the spatial similarity of adjacent pixels. As depicted in Figure 5, the S2D method divides the image into multiple 2 \(\times\) 2 sub-regions and allocates the four pixels from each sub-region to four separate channels in a depthwise-connection manner. By rearranging the elements of the feature map, this algorithm effectively transforms spatial information into the depth (channel) dimension, facilitating downsampling without any loss of information. This technique preserves all the data within the channel dimension, mitigating the information loss typically associated with conventional downsampling methods. Additionally, the implementation of the S2D algorithm enhances image feature extraction, yielding richer and more precise information for the model. As a result, this contributes to improved prediction accuracy and precision for the generator when analyzing radar echo images. Figure 4: Schematic of inter-frame dynamic attention network. The network architecture and data flow for the temporal and spatial discriminators are illustrated in Figure 6. Initially, the discriminator receives 20 consecutive single-channel radar echo images, each with dimensions of 128 \(\times\) 128, resulting in an input data shape of (20, 1, 128, 128). Subsequently, the discriminator network conducts spatiotemporal feature extraction on these data and generates a classification result. In the scenario where the last 10 frames of the 20-frame image sequence consist of real observed images, the discriminator is expected to output 1. Conversely, when the last 10 frames are images predicted by the generator, the discriminator is expected to output 0. The input data for the spatial discriminator are structured in the shape of (20, 1, 128, 128). Initially, these data are transformed via the S2D operation, resulting in a new shape of (20, 4, 64, 64). Following this transformation, the data are processed through four inception modules, which serve to extract spatial features from the radar echo sequence while also performing downsampling, ultimately reshaping the data to (20, 384, 4, 4). The final stages of processing involve the data passing through a convolutional layer, a pooling layer, and a fully connected layer, which collectively assess the authenticity of the data. The temporal discriminator effectively integrates the temporal dimensions of the data during the feature extraction process. Initially, the input data are structured as (20, 1, 128, 128), which is subsequently transformed to (20, 4, 64, 64) following the S2D operation. The data then progress through two 3D convolution modules, resulting in a final shape of (5, 96, 16, 16). As illustrated in Figure 7, a key distinction between 3D convolution and 2D convolution is the incorporation of temporal elements within the data.