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This paper proposes a geometry-aware multi-support heterogeneous graph neural network for fine-scale rainfall field reconstruction, which fuses observations from point gauges, path-integrated microwave links, and gridded radar/satellite data. The method reduces RMSE by 23.2% over classical interpolation on Singapore data and shows greatest gains when the field is undersampled relative to its spatial correlation length.