Amidst escalating pressures from anthropogenic disturbances and climate change, understanding the spatiotemporal dynamics of ecosystem structure-function relationships is fundamental for advancing conservation and sustainable development. While existing studies have explored aspects of either ecosystem structure or function, integrated analyses of their overall synergistic coordination remain limited. To address this knowledge gap, we proposed an integrated framework using the coupling coordination degree (CCD) to quantify the interaction and coordinated development of ecosystem structure and function in China’s drylands. Leveraging high-resolution, multi-source remote sensing data, we combined Random Forest modeling with partial correlation analysis to identify key drivers and project future CCD trajectories under four IPCC climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). From 2000 to 2020, CCD exhibited a pronounced northwest-to-southeast gradient, with 66.9% of the regions showing improvement and 23.5% experiencing decline. The aridity index (AI) was identified as the predominant determinant of CCD variability, demonstrating a positive relationship in 96.2% of the study area, a pattern driven by moisture-mediated constraints on ecosystem self-organization. Future projections reveal a concerning divergence: under fossil-fueled development scenarios (SSP5-8.5), 52.42% of regions are anticipated to undergo CCD deterioration by mid-century, compared to 44.56% under sustainable development (SSP1-2.6). Notably, the regulatory influence of AI on CCD is predicted to intensify under moderate-to-high emission scenarios, underscoring the escalating role of drought as a critical force governing ecosystem stability. These findings provide novel mechanistic insights into the complex internal interactions of ecosystems under climate change. For moisture-limited ecosystems, our results underscore the imperative for adaptive management strategies that enhance hydrological regulation and promote drought-resilient species assemblages.

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