A global, temporally continuous, hourly CO₂ concentration dataset at GAW monitoring stations for 2000–2024: Enabling fine-scale carbon dynamics analysis

Abstract. 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).