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.