Wildfire risk in a changing climate: Evaluating fire weather indices and their global patterns with CMIP6 multi-model projections
Bibliographic record
Abstract
This study investigates potential wildfire risks across different global warming scenarios through a comparative analysis of two prominent fire weather indices: the McArthur Forest Fire Danger Index (FFDI) and the Canadian Forest Fire Danger Index (FWI), leveraging the latest multi-model projections from the 6th phase of the Coupled Model Intercomparison Project (CMIP6). Utilizing the Extreme Gradient Boosting (XGBoost) algorithm and the Shapley value, we identify the impacts of meteorological variables on fire weather danger as represented by FFDI and FWI. Our findings reveal that under the Shared Socioeconomic Pathways (SSP) 5–8.5 high-emission scenario, both FFDI and FWI project significant intensification of fire weather, particularly in historically recognized high-risk hotspots, demonstrating robust inter-model consistency. Notably, the future projections of FFDI indicate the likely occurrence of wildfires with unprecedented severity. The comparative analysis using Shapley values highlights substantial regional and index-specific variations in the contribution of meteorological input variables to fire weather simulations. While these global patterns are generally retained as global warming leads to a systematic reinforcement of all variables, in-depth regional scale analyses further uncover a stark contrast of dominant factors controlling FFDI and FWI. These findings stimulate discussion on the potential adaptability and discrepancies of empirically derived fire models, highlighting the need for future research to advance fire weather modeling with enhanced flexibility and multi-factor consideration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".