Future variations of global vegetation are of paramount importance for the socio-ecological systems. However, up to now, it is still difficult to develop an approach to project the global vegetation considering the spatial heterogeneities from vegetation, climate factors, and models. Therefore, this study first proposes a novel model framework named GGMAOC (grid-by-grid
multi-algorithms
optimal combination) to construct an optimal model using six algorithms (i.e., LR: linear regression
SVR: support vector regression
RF: random forest
CNN: convolutional neural network
and LSTM: long short-term memory
transformer) based on five climatic factors (i.e., Tmp: temperature
Pre: precipitation
ET: evapotranspiration, SM: soil moisture, and CO