A Positive-Only Conditional Normalizing Flow to Assess Lightning-Caused Fires According to Meteorological Condition

Lightning-caused fires (LCFs) are an important forest disturbance closely associated with meteorological conditions. However, reliable non-fire samples are difficult to define because the absence of recorded fires does not necessarily indicate unsuitable fire conditions, limiting conventional binary classification. The purpose of this study is to develop a positive-only framework for assessing the meteorological compatibility of LCFs using verified historical fire records. Using 984 verified LCF records from the Daxing’anling forest region, China, a Positive-Only Spatiotemporal Conditional Normalizing Flow with Context-Mismatch Learning (PO-STCNF-CM) was developed to characterize LCF-associated meteorological conditions across spatial and seasonal contexts. The model incorporates same-day event weighting and context-mismatch learning, and a Meteorological Compatibility Index (MCI) converts density-based anomaly scores into an interpretable 0–100 scale. GMM provided strong likelihood fitting, while ablation analysis showed that spatiotemporal conditioning improved neural density fitting and context-mismatch learning strengthened contextual discrimination. For the held-out records, PO-STCNF-CM produced a mean MCI of 53.761, a median MCI of 57.169, and an 80% coverage error of 0.015. The proposed framework provides an interpretable approach for characterizing LCF-associated meteorological conditions without requiring artificial non-fire samples.