Downscaled CMIP5 projections of physical fire risk understate historical trends

Journal Article · Fri Jul 04 00:00:00 EDT 2025 · Environmental Research Letters

DOI:https://doi.org/10.1088/1748-9326/ade892· OSTI ID:2571920

Avila, Theo C. ^[1]; Lafferty, David C. ^[2]; Stover, Madeline ^[1]; Guerrero, Spencer ^[1]; Samantula, Charumeghana ^[1]; Wing, Ian Sue ^[3]; Sriver, Ryan L. ^[1]

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1. University of Illinois at Urbana‐Champaign, IL (United States)
2. Cornell University, Ithaca, NY (United States)
3. Boston University, MA (United States)

Reliable projections of wildfire risk are important for multi-sector impacts analysis. Statistically downscaled and bias-corrected Earth system model ensemble products are routinely used to analyze regional physical wildfire risk, but evaluations of historical observed trends and variability are lacking. Here, we evaluate physical fire risk over the western United States using the Canadian Forest Fire Weather Index (FWI) by comparing model outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), statistically downscaled via the Multivariate Adaptive Constructed Analogs (MACA) approach, against the observational target dataset gridMET, a gridded high-resolution surface meteorological product. We analyze multidecadal trends and interannual variability in seasonal average FWI for the historical period and future projections under two emissions scenarios, and we compare MACA-CMIP5 ensemble results with a simple time series model that generates historical and future projections of seasonal FWI based on bootstrapping observed historical trends and variability. Our findings indicate that MACA-CMIP5 accurately captures the magnitude and spatial patterns of seasonally averaged FWI but tends to underestimate historical decadal trends. We show that future increases in fire risk may be underestimated relative to the simple time series model that projects historical variability into the future. We also highlight that model biases in relative humidity contribute significantly to model-data differences. Our results underscore the importance of historical hindcasting exercises for informing broader multi-sector applications.

• Research Organization: Cornell University, Ithaca, NY (United States); University of Illinois at Urbana‐Champaign, IL (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER). Earth & Environmental Systems Science (EESS)
• Grant/Contract Number: SC0022141
• OSTI ID: 2571920
• Journal Information: Environmental Research Letters, Journal Name: Environmental Research Letters Journal Issue: 8 Vol. 20; ISSN 1748-9326
• Publisher: IOP Publishing
• Country of Publication: United States
• Language: English

Related Subjects

FWI
fire weather index
physical wildfire risk
statistical downscaling