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Forecasting geostationary infrared brightness temperature sequences from historical observations is a significant and challenging task. By analyzing these predictions, cloud evolution, convective activity, and atmospheric radiative states can be revealed in advance, offering high potential value in domains such as weather nowcasting, energy management, and disaster monitoring. Recently, artificial intelligence techniques have provided valuable insights into this task. However, as a nascent research area, the lack of a standardized, high-quality benchmark has significantly impeded progress. Moreover, training existing deep learning models for this task remains computationally expensive due to the complexity of their network architectures and modeling mechanisms. To address these challenges, we introduce a new benchmark, FY4ABT, and propose a lightweight prediction model, WavePredNet. Specifically, FY4ABT comprises three sub-datasets designed to respectively evaluate prediction performance under short-term, medium-term, and long-term scenarios. Meanwhile, WavePredNet effectively captures multi-scale dynamics, including both low- and high-frequency components with low computational costs while delivering exceptional performance.