Abstract: Dryland ecosystems in China cover about 40% of the country’s land area. They play a vital role in regulating regional climate, conserving biodiversity, and supporting local livelihoods. These regions are spatially heterogeneous and environmentally sensitive, making them highly vulnerable to climate change and human disturbance. The biophysical structure and ecological processes in these ecosystems fundamentally shape their functional integrity, which in turn influences both global environmental change and socioeconomic development. In line with national strategies for ecological civilization and high-quality development, China has launched large-scale desertification control programs such as the "Three-North Shelterbelt" Program. These efforts have achieved significant progress in improving vegetation cover and restoring degraded land. However, maintaining and enhancing adaptive capacity, functional stability, and resilience of these ecosystems under growing climate variability and human pressure remains a difficult challenge. Guided by land system science, this study develops an integrated monitoring and adaptive governance framework to synergistically improve ecological function and stability in dryland desert ecosystems. We conducted a systematic review of recent international and domestic research on desert ecosystem classification, structural–functional dynamics, nonlinear processes, critical thresholds, and adaptive governance. Our analysis identifies persistent research gaps, including thematic fragmentation, static assessments, and oversimplified interpretations of complex interactions. These limitations have hindered a holistic understanding of nonlinear ecological processes and multi-factor synergies, reducing the usefulness of scientific findings for governance. To address these gaps, we propose a comprehensive research framework built on four themes: remote sensing-based monitoring of desert ecosystems, mechanistic analysis of ecosystem evolution, identification of nonlinear thresholds, and multi-scenario adaptive governance for function and stability enhancement. This framework incorporates a range of innovative methods—such as a coordinated “space-air-ground-web” observation network, spectral mixture analysis for fine-scale component extraction, nested ecosystem typology mapping, and multi-functional metric development. It also includes nonlinear time-series analysis, mechanism-based modeling, structure–function state diagnosis, tipping point detection, dynamic zoning, stability assessment, multi-scenario simulation, and adaptive strategy design. Together, these techniques help clarify the evolutionary mechanisms of landscape patterns, key structures, and functional attributes in dryland ecosystems. They also enable accurate identification of critical thresholds. An integrated early-warning system classifies ecosystem states into “healthy,” “transitional,” and “degraded” categories, supporting targeted monitoring and proactive management. The proposed model emphasizes reflexive and learning-enabled governance, allowing for continual adaptation based on real-time monitoring and predictive scenarios. This approach offers a scientific basis for developing region-specific pathways to enhance stability and implement adaptive governance. It promotes a shift from static and uniform management toward dynamic, context-sensitive, and resilience-based governance in drylands. This research provides a scalable and transferable framework that can improve the effectiveness of ecological restoration programs and help strengthen China’s national ecological security barriers. The insights and methods are also relevant for arid land management worldwide, especially in the context of growing environmental uncertainty and global change.