Nationwide Daily Wildfire Occurrence Prediction Using Time Proxy Variables and the Canadian Fire Weather Index (FWI)

Climate change has intensified global wildfire risks, yet national-scale prediction remains challenging due to the difficulty of consistently monitoring fuel conditions and human ignition factors. This study introduces calendar-based time proxy variables as structural surrogates for these unobservable drivers and integrates them with the Canadian Fire Weather Index (FWI) within a parsimonious framework for seasonally fire-prone regions such as South Korea. Using 15 years of nationwide wildfire records and daily observations from 100 ASOS stations (2011–2025), predictive performance was evaluated across eight models and five feature sets (Time-only, Weather-only, Weather + Time, FWI-only, and FWI + Time). Based on test-set mean AUC, the Time-only feature set reached 0.7374, clearly exceeding the random-classifier baseline (AUC = 0.5) and indicating the independent predictive value of time proxy variables. Furthermore, integrating time proxies with FWI improved performance, with the best model (CatBoost) achieving test AUC = 0.8394 and Recall = 0.6019. Multi-model SHAP analysis revealed complementary contributions of FWI components (53.7% ± 4.7%) and time proxy variables (46.3% ± 4.7%). Overall, the results demonstrate that a simple yet structured input design based on time proxy variables provides meaningful predictive performance for nationwide wildfire early warning systems.