IAM-FIRE: A Climate Emulator–Based Framework to Project Wildfire Impacts and Risks for Integrated Assessment Models

Most Integrated Assessment Models (IAMs) underrepresent dynamic feedbacks from climate-driven disturbances such as wildfires, potentially overestimating the permanence of land-based carbon sinks. In particular, representing the impacts of forest fires is becoming increasingly important, as these are expected to intensify in the future. We introduce IAM-FIRE (Integrated Assessment Model—Fire Impacts & Risks Emulator), a novel framework that enables the projection of wildfire burned area (BA) and carbon emissions (CE) directly from IAM outputs. IAM-FIRE combines a spatial climate emulator, land-use downscaling, vegetation productivity modelling, and an empirical fire model to generate global annual wildfire impacts for arbitrary socioeconomic and emissions scenarios at 0.5° resolution for the period 2020–2100. Calibrated against GFEDv5 observations and using inputs from the Global Change Analysis Model (GCAM), BA and CE projections are reported for four scenarios: SSP1-2.6, SSP2-4.5, SSP3-6.6 and SSP5-7.6. The model reproduces historical global trends for both total BA—including the observed global decline since the early 2000s—and forest BA. Projected fire trajectories differ strongly among scenarios: by 2100, total BA range from 441 Mha year⁻¹ under SSP1-2.6 (decline of −2.22 Mha year⁻² relative to 2020) to 794 Mha year⁻¹ under SSP3-6.6 (increase of +2.3 Mha year⁻² relative to 2020). Corresponding total CE show a similar divergence by 2100 ranging from 1.8 PgC year⁻¹ in SSP1-2.6 (decline of −9.15 TgC year⁻² relative to 2020) to 3.6 PgC year⁻¹ in SSP5-7.6 (increase of +12.70 TgC year⁻² relative to 2020). Socioeconomic development exerts a dominant suppressing effect on wildfire impacts while climate change and CO₂-driven increases in vegetation productivity amplify fire risk. Compared to CMIP6 and FireMIP, IAM-FIRE exhibits greater sensitivity to radiative forcing and a stronger role for human-driven fire suppression, highlighting structural uncertainties in fire projections. IAM-FIRE enables systematic exploration of fire–climate–land feedbacks and supports improved assessments of mitigation permanence and climate risks in future integrated scenarios.