Monitoring global vegetation gross primary productivity (GPP) at an hourly scale is critical for understanding terrestrial carbon dynamics, while recent global GPP products often suffer from limitations in their temporal resolutions. Here, a light use efficiency (LUE) model, integrated with FLUXNET and reanalysis datasets as meteorological inputs, was employed to obtain GPP at both 1-hourly and 6-hourly resolutions. We developed a downscaling algorithm that partitions 6-hourly GPP into 1-hourly estimates by weighting the 6-hourly values according to the hourly cosine of the solar zenith angle and applying linear regression. The algorithm was then applied to global 6-hourly reanalysis-driven GPP maps during 2001-2020. Using GPP simulated from 1-hourly meteorological inputs and eddy covariance (EC) GPP as references, the 6-hourly resolution GPP before and after downscaling were evaluated by mean-absolute-deviation (MAD) and nash-sutcliffe-efficiency (NSE). At 150 sites, results showed that the 6-hourly FLUXNET-driven and reanalysis-driven GPP after downscaling exhibited a significantly stronger correlation (MAD = 0.03 gCm-2h-1, NSE = 0.95) with corresponding 1-hourly estimates than the 6-hourly GPP estimates before downscaling (MAD = 0.06 gCm-2h-1, NSE = 0.83). Compared to EC GPP, the 6-hourly GPP estimates after downscaling also achieved notable improvements, with a MAD lower by 0.02 gCm-2h-1 and an NSE higher by 0.09. At the global scale, the mean annual bias in total GPP summed from 6hourly reanalysis-driven maps, decreased from 4.14 gCyr-1 before downscaling to 0.53 gCyr-1 after downscaling over the period 2001-2020, as compared with corresponding 1-hourly GPP maps. At the hourly scale, the mean relative error between the 6-hourly and corresponding 1-hourly GPP maps decreased from 32.40% before downscaling to 18.49% after downscaling. This downscaling algorithm effectively reduces biases in global GPP estimates, which could offer valuable insights into carbon modeling at finer temporal resolutions.