Abstract Machine learning (ML) offers a promising alternative for weather forecasting by reducing computational costs and modeling complex non‐linear atmospheric processes. While recent foundation models highlight this potential with advanced architectures, interpreting the “black‐box” nature of ML models remains challenging. This study proposes an interpretable ML model combining graph neural networks and multi‐layer perceptrons (MLP). By using the graph targeted for large‐scale movement in the dynamical core, and MLP targeted for small‐scale motion in physical parameterizations, our model provides a new perspective to simulate the transition of variables. Through 10‐day iterative forecasts, our model shows comparable performance to purely data‐driven models when trained at 1.5° resolution, with fewer parameters, and faster training speed than physics‐informed neural networks, like those solving differential equations. Moreover, a case study of the 2020 monsoon demonstrates the model’s interpretability by exploring the correlations between the attentions in graphs and atmospheric processes such as wind and precipitation.

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