A global, temporally continuous, hourly CO 2 concentration dataset at GAW monitoring stations for 2000–2024: Enabling fine-scale carbon dynamics analysis

Global Atmosphere Watch (GAW) carbon dioxide (CO2) observations provide high-precision constraints on surface CO2 variability, but many station records remain temporally incomplete because of flask sampling, instrument interruptions, and quality-control exclusions. Such gaps can limit the representation of diurnal variability, bias daily, monthly, and annual mean CO2 estimates, and reduce the ability to detect short-lived events. Here, we present a global, temporally continuous hourly reconstruction dataset of GAW station CO2 concentrations for 2000–2024. The dataset was generated using a two-stage reconstruction framework that integrates GAW observations with Carbon Tracker (CT) and Copernicus Atmosphere Monitoring Service (CAMS) CO2 fields, together with anthropogenic, vegetation, meteorological, and spatiotemporal predictors. In the intra-day gap filling stage, missing hours on days with at least one valid observation were reconstructed using hour-pair models to produce complete daily profiles. In the full-sequence reconstruction stage, the remaining periods with no valid observations were reconstructed using a globally trained initial prediction model followed by station-specific post-correction. The framework showed robust validation performance at the hourly scale, with R2 = 0.96 and RMSE = 3.65 ppm for intra-day gap filling and R2 = 0.93 and RMSE = 4.76 ppm for full-sequence reconstruction. Across all stations, the mean filling rate was 68.9 %, and the mean reconstruction uncertainty was 2.80 ppm. Applying the World Data Centre for Greenhouse Gases (WDCGG) global analysis framework showed that reconstructed global monthly mean CO2 concentrations were, on average, 0.54 ± 0.21 ppm higher than those derived from the original GAW records, indicating that incomplete temporal coverage can measurably affect global monthly estimates. Compared with grid-cell CT and CAMS simulations, the reconstructed dataset retained stronger station-scale diurnal variability and captured CO2 enhancements during the 2023 Canadian wildfire period. This dataset can support long-term assessments of global and regional surface CO2 changes, fine-scale analyses of CO2 variability, and regional carbon emission inversion and evaluation. The dataset is available at https://doi.org/10.5281/zenodo.21306261 (Mi et al., 2026).