Satellite cloud imagery is pivotal for meteorologists in characterizing weather patterns, detecting climate anomaly regions, and predicting rain effects. The task of satellite cloud image forecasting is crucial, and while deep learning models have shown promise in predicting spatio-temporal data, traditional methods face challenges with extracting long-term spatio-temporal features and high computation costs. To address these issues, we propose the Re-parameterized Sequence-to-Sequence Satellite Cloud Imagery Prediction Network (Rep-SSCIPN). Rep-SSCIPN utilizes Rep-convolution layers to reduce inference-time cost and memory consumption, enhancing efficiency by converting re-parameterized blocks into a single convolution layer during inference. The sequence normalization attention mechanism in Rep-SSCIPN highlights crucial feature sequences and establishes their inter-dependencies. We validate our novel method using a real-world satellite cloud image dataset from the meteorological satellite “Himawari.” Experimental results showcase significant improvements in prediction accuracy and reconstruction quality compared to ConvLSTM, PredRNN, FCLSTM, LMC, SimVP and SCSTque models. The efficiency gains make Rep-SSCIPN a promising advancement for satellite cloud image prediction.ARTICLE INFO.