Global Daily XCO2 Reconstruction at 0.05° Resolution for 2021–2024 Using Satellite and Reanalysis Data

Abstract

Accurate and seamless reconstruction of column-averaged dry-air CO2 mole fraction (XCO ${}_{2}$ ) at high spatial and temporal resolution remains challenging because satellite observations are sparse, unevenly distributed, and frequently interrupted by clouds and retrieval-related limitations. In this study, we propose Trans-XCO2, a spatiotemporal deep learning (DL) framework for reconstructing global daily XCO2 at 0.05° resolution during 2021–2024 by integrating transport background fields with multisource environmental predictors. The framework incorporates instantaneous environmental covariates and short-term atmospheric memory to represent temporal transport effects, and uses a wavelet-enhanced Transformer (WET) to improve multiscale spatial representation. A dual-resolution decoding strategy is further introduced to preserve large-scale background structures while refining fine-scale spatial details. The model is trained using data from 2021 to 2023 and evaluated through strict temporal extrapolation in 2024. Independent validation against measurements from the Total Carbon Column Observing Network (TCCON) shows strong agreement, with an R2 of 0.93 and an RMSE of 1.04 ppm. Compared with coarse-resolution background products, Trans-XCO2 achieves lower reconstruction errors while better preserving spatial gradients. The reconstructed dataset reveals a persistent global XCO2 growth rate of approximately 2.36 ppm yr−1 during 2021–2024, with similar temporal trends over land and ocean. The resulting seamless high-resolution XCO2 dataset provides valuable support for atmospheric carbon monitoring, satellite-model data fusion, and carbon cycle analysis.