Temporal Resolution Conditions the Global–Local Trade-off in Wildfire Spread Prediction

Which inductive bias should a spatiotemporal operator have? We compare a gated global–local operator—spectral graph mixing fused per cell with local convolution—against convolutional recurrent baselines on 27 cross-state wildfire cases, and find that the answer depends on how far the fire front moves per model step. On the slowest fires of the main pool, under per-fire ROC-AUC, the operator ties the recurrent baseline at h=1 and leads it from h=3 on, the margin growing with horizon (+0.006, +0.047, +0.064 on a 6 h lattice); on the fastest it loses, and removing either branch, adding spectral or local capacity, or widening the training ring does not close that gap. A preregistered intervention that doubles per-step travel on the same fires by re-indexing them onto steps of twice the length moves the winner across families (+0.094 ROC-AUC, p=0.0022), so on these fires the dependence is interventional, not merely observed. The mechanism is a division of labor: the global path should predict for slow long-range spread and condition the local path on fast short-range steps, and neither role substitutes for the within-window local recurrence that carries fast regimes. The same ordering holds on the 419-fire WSTS benchmark, where every fire lies at the fast end. The ranking is a property of the (architecture, discretization) pair rather than of the architecture alone. All comparisons are graded verified, faithful, or historical under a protocol-recovery audit.