Daily Gap-Filling of TROPOMI XCH₄ at 5 km Resolution over China

Daily, high-resolution XCH₄ products are essential for verifying emission inventories and detecting transient anomalies, yet TROPOMI retrievals over China suffer from pervasive gaps due to cloud cover, albedo variability, and retrieval quality. We present a daily, gap-free XCH₄ dataset at 5 km resolution over China (2018–2025), reconstructed from TROPOMI using XGBoost augmented with Random Fourier Features (RFF). The RFF projection eliminates the rectilinear mosaic artifacts intrinsic to axis-aligned tree splits, yielding spatially continuous fields suitable for downstream applications. The model integrates ERA5 reanalysis, CAMS methane analysis, MODIS NDVI, and a secular trend term within a feature space spanning chemical, meteorological, and biospheric processes.Variogram-informed spatial-block cross-validation yields R² = 0.860 and RMSE = 13.96 ppb, whereas conventional random cross-validation inflates explained variance by approximately 44%, exposing a pervasive overestimation of predictive accuracy. Independent validation against two TCCON stations entirely excluded from the training pipeline achieves R² = 0.835 and RMSE = 12.57 ppb (N = 1,245), expanding daily matchup availability 2.4-fold and reducing systematic bias from +4.33 to +2.08 ppb relative to the original quality-filtered TROPOMI retrievals. SHAP analysis confirms that temperature and soil-moisture responses dominate the learned feature hierarchy, consistent with methanogenesis biogeochemistry. The reconstructed dataset resolves short-lived anomalies invisible in the raw satellite record, including the 2022 Yangtze drought suppression and 2020 COVID-19 lockdown perturbations over Shanxi Province. The product is suitable for regional emission monitoring, anomaly detection, and as prior fields for flux inversions.