Extreme Climate Drivers and Their Interactions in Lightning-Ignited Fires: Insights from Machine Learning Models
Bibliographic record
Abstract
Lightning is the primary natural cause of wildfires in mid- to high-latitude forests, and it is increasing in frequency under climate change. Traditional fire danger forecasts, reliant on standard meteorological data, often fail to capture extreme events and future risk. To address this issue, we integrate extreme climate indices with meteorological, vegetation, soil, and topographic data, and apply four machine learning methods to build probabilistic models for lightning fire occurrence. The results show that incorporating extreme climate indices significantly improves model performance. Among the models, XGBoost achieved the highest accuracy (87.4%) and AUC (0.903), clearly outperforming traditional fire weather indices (accuracy 60%–71%). Model interpretation with SHapley Additive exPlanations (SHAP) further revealed the driving mechanisms and interaction effects of extreme factors. Extreme temperature and precipitation indices contributed nearly 60% to fire occurrence, with growing season length (GSL), minimum of daily maximum temperature (TXn), diurnal temperature range (DTR), and warm spell duration index (WSDI) identified as key drivers. In contrast, heavy precipitation indices exerted a suppressing effect. Compound hot and dry conditions amplified fuel aridity and markedly increased ignition probability. This interpretable framework improves short-term lightning fire prediction and offers quantitative support for risk warning and resource allocation in a warming climate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".