Abstract:In the context of intensified climate change, exploring the driving mechanism of climate factors on the evolution of blue-green spatial patterns is significant for achieving sustainable development. This study uses three periods of land use data from 1980, 2000, and 2020 and climate data from 1980 to 2020. Based on methods such as climate tendency rate, dynamic change model, Markov transition matrix, and landscape pattern index. Using ArcGIS and FRAGSTATS software, the spatiotemporal evolution characteristics of climate factors and landscape patterns in the blue-green space of the Chengdu metropolitan area are analyzed, and the driving mechanism of climate factors on landscape pattern changes is explored through the grey correlation method. The results show that: (1) From 1980 to 2020, the average annual temperature, precipitation, and potential evapotranspiration in the Chengdu metropolitan area were positively correlated with time, with only a negative correlation between the average annual sunshine hours and time. In terms of spatial distribution, the evolution of four meteorological factors mainly occurred in mountainous and urban areas. (2) The blue-green space area in the Chengdu metropolitan area decreased by 22.74 km2 from 1980 to 2000 and increased by 62.45 km2 from 2000 to 2020. The fragmentation of blue-green space intensified, the complexity of patch edges increased, the landscape types increased, and the distribution became more uniform. (3) The average correlation between climate factors and the blue-green spatial landscape pattern index, in descending order, was annual potential evapotranspiration (0.78), annual sunshine hours (0.76), annual precipitation (0.74), and annual temperature (0.72), indicating that climate factor changes had a strong driving effect on the evolution of blue-green spatial landscape patterns. The research results can provide a scientific basis and reference for planning and managing blue-green space in the Chengdu metropolitan area and constructing a climate adaptive pattern. Subsequently, it is intended to include factors such as natural geography and socio-economic factors for comparative analysis of driving mechanisms. It will also explore areas such as climate adaptive landscape pattern construction, climate change risk assessment, and multi-scale climate adaptive response.