<p>To improve the fusion modeling capability and prediction accuracy of multimodal marine meteorological data in complex environments, this paper proposes a cross-scale fusion method for multimodal marine meteorological data (MMAF). First, to address the problem of inconsistent resolution of data from different modalities, a cross-scale feature consistency modeling mechanism is proposed. Through multi-resolution feature extraction and scale normalization processing, the modeling capability of heterogeneous modalities in a unified feature space is achieved. Secondly, to address the inconsistency in feature distribution of multimodal data, a multimodal alignment method combining maximum mean difference and adversarial learning is constructed. Through global distribution matching and modal discrimination confusion, it guides the efficient alignment of multimodal data in a shared embedding space. Finally, to address the possible redundancy, conflict and missing problems between modal information, a multimodal data anomaly perception and processing mechanism is designed to achieve robust multimodal fusion expression. Experimental results demonstrate that the proposed method can effectively improve alignment and fusion accuracy, and reduces the MSE index by approximately 15.35% compared to other methods in downstream prediction tasks, indicating that it has good generalization ability and application potential.</p>