| 13 Feb 2026
Reconstructing two-decadal global daily high-resolution XCO2 records using a hybrid Transformer–BiLSTM model
Abstract. Accurate and temporally continuous global observations of atmospheric carbon dioxide (XCO2) are essential for climate monitoring and emission assessment. However, satellite-based XCO2 observations are often spatially incomplete and temporally discontinuous, while existing products typically suffer from coarse spatial resolutions, hindering the detection of fine-scale emission changes. Here, we developed a novel spatiotemporal Transformer-BiLSTM deep-learning network, which integrates the local temporal feature extraction capability of bidirectional long short-term memory with the global spatial dependency modeling strength of Transformer via self-attention mechanisms. The network assimilates multisource data, from satellite observations, meteorological reanalysis, and precursor gases, to reconstruct global, daily, and seamless XCO2 at 0.1° resolution from 2003 to 2022. Validation against independent Total Carbon Column Observing Network (TCCON) measurements shows excellent agreement, with a correlation coefficient (R2) value of 0.99, a root mean square error (RMSE) of 1.10 ppm, and a mean bias of 0.01 ppm. A subsequent bias correction scheme further improves cross-satellite consistency, achieving a cross-validation coefficient of determination (CV-R2) of 0.99 and an RMSE of 0.97 ppm. Our dataset enables accurate characterization of daily XCO2 concentrations over global land surfaces, facilitating the detection of spatial heterogeneity associated with emission hotspots and point-source activities. The record reveals a persistent global increase in atmospheric XCO2 over the past two decades, with a mean growth rate of 2.24 ppm/yr (p < 0.001). It reliably resolves global XCO2 variability across a wide range of temporal scales, from day-to-day fluctuations to long-term trends. It consistently captures large-scale climate-driven signals, such as ENSO-related interannual variability, and short-lived XCO2 enhancements associated with major wildfire events, demonstrating its capability to represent both persistent and episodic emission signals. This high-resolution, daily global XCO2 product (GlobalHighXCO2) provides a valuable benchmark for carbon cycle studies, atmospheric model evaluation, and emission monitoring, and is publicly available at https://doi.org/10.5281/zenodo.18220961 (Qu and Wei, 2026).
Competing interests: At least one of the (co-)authors is a member of the editorial board of Earth System Science Data.
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