Signal-Domain Guided Deep Learning for Gap-Filling of XCO and XCH₄: A Masked Spatio-Temporal Fusion of TROPOMI and GEOS-Chem (2019–2023)

Abstract. Long-term, high-resolution monitoring of carbon monoxide (CO) and methane (CH₄) is essential for understanding their spatiotemporal variability and guiding climate mitigation strategies. However, satellite observations like TROPOMI are often incomplete, and existing fusion methods have limitations in accuracy and continuity. This study proposes a signal-domain fusion approach combining 3D discrete cosine transform (DCT) and singular value decomposition (SVD) to integrate TROPOMI data with GEOS-Chem simulations. A lightweight residual U-Net is employed to refine the initial reconstruction by learning the residual field using meteorological drivers and model outputs, guided by a masked loss. The method produces global 0.25° and China-specific 0.05° daily gap-free XCO and XCH₄ datasets from 2019 to 2023. The fused results outperform GEOS-Chem and are comparable or superior to TROPOMI, with R² values of 0.92 for XCO and 0.85 for XCH₄. Trend analysis reveals regional patterns such as XCO increases in North America and declines in Eastern China, and widespread CH₄ growth. High-resolution data captures enhancements during the 2022 Chongqing wildfires, with average increases of 17.1 ppb in XCO and 24.5 ppb in XCH₄, and reveals lower XCH₄ increases over rice-growing areas compared to TROPOMI, with overestimation reduced by 17–26 %, and stronger XCO reductions, with satellite underestimations up to 38 %. These results highlight agricultural contributions and policy impacts. This approach effectively reconstructs missing observations and enhances the utility of satellite–model data for atmospheric research and emission assessments. The generated daily gap-free datasets are publicly available at https://doi.org/10.5281/zenodo.17936461.