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This paper proposes a novel atmosphere-informed predictive satellite channel model for beyond the fifth-generation (B5G)/the sixth-generation (6G) satellite-terrestrial wireless communication systems at Q-band to model/predict channel attenuation at any specific time. The proposed channel model is a data-driven model based on either of two deep learning networks, i.e., multi-layer perceptron (MLP) and long short-term memory (LSTM). The accuracy of the proposed channel model is measured by cumulative density function (CDF) of absolute error and mean square error (MSE) between modeled/predicted and measured channel attenuation. The complexity of the proposed channel model is assessedby the training time, loading time, and test time of deep learning networks. To further improve the accuracy of the proposed channel model, weather classification is developed at the stage of database construction …