Evaluating region-dependent skill of seasonal Fire Weather Index forecasts in Australia

Forecasting fire danger is essential for early warning, fire management, and planning in several climate-sensitive industries. In Australia, fire regimes are highly seasonal and regionally diverse, creating a complex land-atmosphere interaction driven by extreme climate variability. This study is a preliminary investigation into the relationship between MODIS burned-area data and datasets that can act as predictors, such as seasonal Canadian Fire Weather Index (FWI) forecasts on multiple lead times from the ECMWF; coupled ocean-atmosphere climate modes – Indian Ocean Dipole (IOD) and El Niño-Southern Oscillation (ENSO); satellite-derived fuel-related variables NDVI, NBR, FAPAR at national and subnational (climate biome) scales, to inform the development of a region-adaptive forecasting framework.

Spatio-temporal correlation and spatial autocorrelation are assessed between gridded datasets, with time-series analysis focusing on lagged teleconnections and cross-correlation. In the case of the forecast-driven FWI diagnostic comparisons with reanalysis FWI is undertaken to provide context for forecast skill. These diagnostics are employed to investigate whether Australian fire regimes are governed by a dual-constraint system with a fuel-accumulation and climate-driven phase, in which antecedent fuel accumulation as well as weather triggers are the primary drivers.

The purpose of this study is to reveal the extent to which FWI’s ability to predict danger varies across biomes, highlighting the need for fuel-related inputs. Lagged analysis is used to inform the optimal temporal scale for predicting fire danger in Australia. Diagnostic comparison with reanalysis data may identify potential biases in the ECMWF forecast dataset that play a role in its relationship with burned area, further highlighting the need for a region-adaptive framework to correct for local land-mediated influences. These preliminary findings will shape ongoing research into the use of different combinations of variables by regions.