To address the limitation of existing transient waveform image-based transmission line fault cause identification methods, namely, that the use of single-type input features prevents fine-grained fault cause classification, this paper proposes a novel fault cause identification method based on multimodal residual network (ResNet). The method integrates transient waveform features with weather characteristics. First, the characteristics of different causes of transmission line faults are analyzed statistically in terms of both transient waveform and weather conditions. Second, transient waveform images and one-hot codes for weather conditions at the time of the fault are used as inputs to an improved multimodal ResNet classifier. A channel attention mechanism is used to fuse the extracted fault transient waveform image features and weather features, enabling training and testing of the fault identification model. Finally, real fault recording data are used to perform case study verification. The results show that the proposed method achieves a fault cause identification accuracy of 94.87%. Compared with traditional fault identification methods, it requires fewer fault features, offers superior discrimination for easily confusable fault types, and provides significantly higher identification accuracy.