Distinguishing gross primary production of sunlit and shaded leaves (GPP_sun and GPP_shade) is crucial for improving our understanding of the underlying mechanisms regulating long-term GPP variations. Here we produce a global 0.05°, 8-day dataset for GPP, GPP_shade and GPP_sun over 1992-2020 using an updated two-leaf light use efficiency model (TL-LUE), which is driven by the GLOBMAP leaf area index, CRUJRA meteorology, and ESA-CCI land cover. Our products estimate the mean annual totals of global GPP, GPP_sun, and GPP_shade over 1992-2020 at 125.0±3.8 (mean ± std) Pg C a^-1, 50.5±1.2 Pg C a^-1, and 74.5±2.6 Pg C a^-1, respectively, in which EBF (evergreen broadleaf forest) and CRO (crops) contribute more than half of the totals. They show clear increasing trends over time, in which the trend of GPP (also GPP_sun and GPP_shade) for CRO is distinctively greatest, and that for DBF (deciduous broadleaf forest) is relatively large and GPP_shade overwhelmingly outweighs GPP_sun. This new dataset advances our in-depth understanding of large-scale carbon cycle processes and dynamics.

The dataset used a revised two-leaf light use efficiency (TL-LUE) model with an added CO₂ concentration regular scalar and the modified temperature scalar. These products are driven by the GLOBMAP leaf area index (GLOBMAP-LAI), Climatic Research Unit and Japanese reanalysis (CRUJRA 2.2) meteorological data, and European Space Agency Climate Change Initiative Land Cover (ESA-CCI) data.

The units of three temporal resolutions (8-day, monthly, annual) are gC m^-2 8day^-1, gC m^-2 month^-1 and gC m^-2 a^-1, respectively. And the scale factor of the monthly data is 0.1, that of the 8-day data is 0.01. In the dataset, in order to ensure the authenticity, we did not delete or modify a small number of abnormally high values (caused by LAI). Therefore, when using this dataset, you can set thresholds to remove the anomalies.