极端降雨会迅速降低路网功能（RNF），扰乱应急出行、可达性、疏散、物流及基础设施恢复。现有的洪水-交通影响模型往往难以跨区域部署，因为它们依赖本地校准、高强度的模拟工作流程或目标区域的重新训练。本研究提出 G-R2R，一种可迁移的时空图学习框架，将降雨引发的路网中断重构为针对未见交通网络的区域条件化、拓扑灵活图预测问题。该框架整合了以自我为中心的子图采样、地形条件化的图表征、区域条件化的双层静态编码以及动态降雨-网络特征学习。通过将静态地理和基础设施属性与降雨强迫及近期 RNF 状态融合，实现对异构路网节点级 RNF 的预测。G-R2R 在中国温州十个易洪区域的模拟衍生 RNF 序列上进行评估，采用未见区域测试协议。在固定的未见区域基准下，其性能优于代表性预测基线，稳定 MAPE 达到 2.33%，$R^2$ 达到 0.855。留一法区域评估得出的稳定 MAPE 值为 2.30%-3.54%。消融分析支持了地形条件化图表征和双层静态编码的贡献。对菲特台风历史事件的重演进一步表明，G-R2R 无需目标区域重新训练，即可在观测到的降雨强迫下复现模拟衍生 RNF 参考的主要时间轨迹和节点级空间模式。这些结果支持在本研究涵盖的区域路网域内进行快速且可迁移的节点级 RNF 预测。

Extreme rainfall can rapidly degrade road-network functionality (RNF), disrupting emergency mobility, accessibility, evacuation, logistics, and infrastructure restoration. Existing flood-transportation impact models are often difficult to deploy across regions because they rely on local calibration, intensive simulation workflows or target-region retraining. This study proposes G-R2R, a transferable spatiotemporal graph-learning framework that reframes rainfall-induced road-network disruption as a region-conditioned, topology-flexible graph forecasting problem for unseen transportation networks. The framework integrates ego-centered subgraph sampling, terrain-conditioned graph representation, region-conditioned dual-level static encoding, and dynamic rainfall-network feature learning. Static geographic and infrastructure attributes are fused with rainfall forcing and recent RNF states to forecast node-level RNF across heterogeneous road networks. G-R2R is evaluated using simulation-derived RNF sequences from ten flood-prone regions in Wenzhou, China, under unseen-region testing protocols. It outperforms representative forecasting baselines, achieving a stabilized MAPE of 2.33% and $R^2$ of 0.855 under the fixed unseen-region benchmark. Leave-one-area-out evaluation yields stabilized MAPE values of 2.30%-3.54%. Ablation analysis supports the contributions of the terrain-conditioned graph representation and dual-level static encoding. A historical-event replay of Typhoon Fitow further shows that G-R2R reproduces principal temporal trajectories and node-level spatial patterns of the simulation-derived RNF reference under observed rainfall forcing without target-region retraining. These results support rapid and transferable node-level RNF forecasting across the regional road-network domains represented in this study.