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Description: Leaf Area Index (LAI) is a critical parameter in terrestrial ecosystems, with high spatial reso-lution data being extensively utilized in various research studies. However, LAI data under future scenarios are typically only available at 1° or coarser spatial resolutions. In this study, we generated a dataset of 0.05° LAI (F0.05D-LAI) from 1983-2100 in a high spatial resolu-tion using the LAI Downscaling Network (LAIDN) model driven by inputs including air tem-perature, relative humidity, precipitation, and topography data. The dataset spans the his-torical period (1983-2014) and future scenarios (2015-2100, including SSP-126, SSP-245, SSP-370, and SSP-585) with a monthly interval. It achieves high accuracy (R² = 0.887, RMSE = 0.340) and captures fine spatial details across various climate zones and terrain types, indi-cating a slightly greening trend under future scenarios. F0.05D-LAI is the first high-resolution LAI dataset and reveals the potential vegetation variation under future scenarios in China, which benefits vegetation studies and model development in earth and environ-mental sciences across present and future periods.