Fine-resolution estimation of gross primary production (GPP) is essential for advancing our knowledge of ecosystem carbon cycling. However, most existing global GPP products are constrained by coarse temporal resolutions (typically >= 8 days), limiting their capacity to capture short-term variations in ecosystem productivity. This paper presents a global new GPP dataset for 2001-2020, based on a modified radiation scalar two-leaf LUE (RTL-LUE) model. The RTL-LUE GPP dataset is generated at an hourly temporal resolution and a spatial resolution of 0.1 degrees, driven by the hourly climate data from ERA5-land, GLASS leaf area index, MODIS land cover, and NOAA atmospheric CO2 concentration. During 2001-2020, this dataset provides a slightly lower global total GPP (124.77 PgC/yr) than the 8-day TL-LUE GPP dataset (126.92 PgC/yr), primarily due to its ability to capture short-term extreme stresses more effectively. Notably, this dataset reveals that annual GPP variability can reach up to approximately 0.10 g C/m2 /h at hourly scales. The RTL-LUE GPP demonstrates robust performance at 184 towers, thereby improving insights into the temporal dynamics of carbon fluxes.