The existing medium-resolution land cover time series produced under the European Space Agency's Climate Change Initiative provides 29 years (1992-2020) of annual land cover maps at 300m resolution, allowing for a detailed study of land change dynamics over the contemporary era. Because models need two-dimensional parameters rather than two-dimensional land cover information, the land cover classes must be converted into model-appropriate plant functional types (PFTs) to apply this time series to Earth system and land surface models. The first-generation cross-walking table that was presented with the land cover product prescribed pixel-level PFT fractional compositions that varied by land cover class but that lacked spatial variability. Here we describe a new ready-to-use data product for climate modelling: spatially explicit annual maps of PFT fractional composition at 300m resolution for 1992-2020, created by fusing the 300m medium-resolution land cover product with several existing high-resolution datasets using a globally consistent method. In the resulting data product, which has 14 layers for each of the 29 years, pixel values at 300m resolution indicate the percentage cover (0%-100%) for each of 14 PFTs, with pixel-level PFT composition exhibiting significant intra-class spatial variability at the global scale. We additionally present an updated version of the user tool that allows users to modify the baseline product (e.g. re-mapping, re-projection, PFT conversion, and spatial sub-setting) to meet individual needs. Finally, these new PFT maps have been used in two land surface models - Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) and the Joint UK Land Environment Simulator (JULES) - to demonstrate their benefit over the conventional maps based on a generic cross-walking table. Regional changes in the fractions of trees, short vegetation, and bare-soil cover induce changes in surface properties, such as the albedo, leading to significant changes in surface turbulent fluxes, temperature, and vegetation carbon stocks. The dataset is accessible at 10.5285/26a0f46c95ee4c29b5c650b129aab788 (Harper et al., 2023).