###### Abstract
[6] developed the predictive recurrent neural network (PredRNN), which utilizes a spatiotemporal LSTM (ST-LSTM) unit to simultaneously extract and store spatiotemporal representations. [PERSON] et al. [7] expanded on this with PredRNN++, featuring cascaded dual memories based on a causal long short-term memory (Causal-LSTM) layer to deep time-step recurrence and enhance gradient propagation. [PERSON] et al. [8] proposed the eidetic 3D LSTM (E3D-LSTM), integrating 3D convolution into recurrent neural networks (RNNs) to improve short-term feature retention while using a gated self-attention module for long-term relationships. [PERSON] et al. [9] addressed the challenge of utilizing differential signals in recurrent neural networks with the memory in memory (MIM) network, which employs two self-updating memories to model both non-stationary and stationary properties in spatiotemporal dynamics. To effectively analyze the spatiotemporal dynamics inherent in time series data, researchers can project input frames into a latent space and employ RNNs for forecasting subsequent frames. [PERSON] et al. [10] developed a spatiotemporal predictive autoencoder that utilizes convolutional long short-term memory (ConvLSTM) units, emphasizing the detection of motion variations and incorporating an optical flow prediction module. In a different approach, [PERSON] et al. [11] integrated a two-dimensional convolutional neural networks (CNN) encoder with a ConvLSTM decoder within the framework of variational recurrent neural networks (VRNN), thereby enhancing the expressiveness of the latent distribution. Although resolution-preserving blocks are effective in maximizing information retention, they necessitate substantial memory resources. [PERSON] et al. [12] introduced a conditional reversible network (CrevNet) that features a dual-target autoencoder and a CNN-based normalizing flow, aimed at improving the capture of temporal dependencies. Additionally, [PERSON] et al. [13] proposed the decompositional disentangled predictive autoencoder (DDPAE), which disaggregates high-dimensional video data into low-dimensional temporal dynamics for the purpose of unsupervised learning. Furthermore, [PERSON] et al. [14] presented a dual-branch architecture that merges physically recurrent cells with ConvLSTM networks, facilitating predictions that are constrained by partial differential equations within the latent space. Recent advancements in deep learning have significantly improved the performance of spatiotemporal sequence prediction. However, recursive generation of images can lead to blurriness due to cumulative errors. Generative adversarial networks (GAN) [15] mitigate this issue by leveraging adversarial learning to produce images that are closer to reality. [PERSON] et al. [16] designed a model using a multi-scale convolutional recurrent neural network, allowing the model to predict image sequences with more detailed information due to its multi-scale design. [PERSON] et al. (2017) introduced the retrospective cycle GAN, which uses a single generator to predict future and past frames, ensuring bidirectional prediction consistency with a retrospective cycle constraint. [PERSON] et al. (2018) developed a GAN with two generators to learn mappings between source and target images without paired examples. [PERSON] et al. (2019) proposed a dual-GAN mechanism, enabling unsupervised training from two unlabeled image sets by using one GAN to map original images to predicted ones and another to reverse-map them, allowing for reconstruction across both domains. Currently, radar-echo-extrapolation-based nowcasting of precipitation faces several key challenges:  
(1) Complex Dynamic Evolution of Radar Echoes: The intricate spatiotemporal dynamics associated with the evolution of radar echoes present substantial obstacles for deep learning models to effectively capture, particularly in the context of rapidly changing weather systems.