An Open National Benchmark for Forecasting Registered Wildfire Occurrence

Machine-learning benchmarks for wildfire forecasting are built almost entirely on satellite fire observations, yet where those products have been compared with agency records they detect a minority of the smaller fires those registers hold, and detection improves steeply with fire size. We release an open benchmark for forecasting where forest fires will be registered: 83 regions of Russia, selected by the continuity of their fire records, 64,620 hexagonal cells of about 253 km², daily over the April to October seasons of 2000 to 2024, labelled with 420,995 registered fires; 0.11% of cell-days carry at least one. Every input is openly licensed and redistributable, with weather sampled on a fixed hierarchical grid that keeps every cell within 68 km of its weather, so the dataset can be published in full and served in real time from the same sources. The protocol is built for extreme imbalance: a temporal split, evaluation on the complete national grid rather than on sampled negatives, and metrics a dispatcher can act on, above all the share of the day's fire cells caught inside a fixed alarm budget. Across 807 days of four fire seasons, a gradient-boosted baseline catches 80.5% of the day's fire cells while flagging 15.2% of the grid, against 63.7% for climatology, 53.6% for the Canadian Fire Weather Index and 47.9% for the Russian statutory index at the same budget; it beats climatology on 97% of individual days and leads at every budget we scored, from 1% to 25% of the grid. Capture varies little across fire size, so the model finds small fires as well as large ones. Matched-representation experiments then show that the hand-designed fire-weather summaries this field relies on, including the statutory index, are replaceable: the same model given the raw 30-day weather series in their place matches or beats them, while a recurrent model and a graph over weather channels show no consistent advantage over it.