Lightning is a major natural ignition source of wildfires across forest, grassland, and cropland ecosystems. Accurate prediction of lightning-ignited fire occurrence remains challenging due to uncertainties in spatiotemporal alignment caused by vegetation-dependent smoldering delays and the difficulty of representing heterogeneous fuel conditions in mixed-vegetation regions. This study proposes a semi-automated lightning–fire alignment framework that integrates land cover information and historical fire records to improve spatiotemporal matching across different vegetation types and to reduce misclassification from human-induced fires in agricultural areas. To better characterize fuel conditions, two feature-level vegetation fusion parameters—total vegetation cover and leaf area index weight—are introduced and combined with hourly meteorological variables and lightning characteristics to develop a tuned random forest prediction model. The framework is applied at a regional scale in the Greater Khingan Mountains and southwestern forest regions of China, with predictions conducted at an event-based temporal scale using hourly inputs. The vegetation-fused model achieves an AUC of 0.93, outperforming models without vegetation fusion. Analysis of model outputs indicates that hourly maximum temperature, leaf area index weight, precipitation, and wind speed are key factors influencing lightning-ignited fire occurrence. This study demonstrates the value of semi-automated alignment and vegetation feature fusion for improving lightning-ignited fire prediction in heterogeneous landscapes, supporting regional wildfire risk assessment and potential early-warning applications.

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.