Training, inference, and evaluation code for a Factorized Fourier Neural Operator (F-FNO) surrogate model of basin-scale tsunami propagation in the East Sea (Sea of Japan).Companion archive for: Kim et al., "A Factorized Fourier Neural Operator Surrogate for Basin-Scale Tsunami Propagation", Geoscientific Model Development, 2026. This archive contains two files: 1. ffno-tsunami-v1.0.0.zip (~421.6 MB) — Source code and model weights - train.py: full training code (architecture, loss, training loop) - inference.py: autoregressive rollout inference and figure generation - convert_comcot_to_nc.py: COMCOT raw output to NetCDF conversion - split_loader.py: function for loading train/val/test split lists in train.py - Pretrained model weights (.pt), two configurations - Scenario parameter table (864 logic-tree configurations) - COMCOT control file template and input generation script - Train/val/test split list files 2. ffno-tsunami-test-EM-data.zip (~44.12 GB) — Test-EM evaluation dataset - 54 NetCDF files for the most challenging test split (unseen epicenter + unseen magnitude, Ep 1 × Mw 8.0) - Sufficient to reproduce all Test-EM results reported in the paper The full training dataset (~642 GB, 864 scenarios) can be regenerated from the provided scenario parameters using COMCOT v1.7 and is available from the authors upon request.