Abstract: Dryland can cover about 40% of the land area in China. The dryland has been widely used to regulate the regional climate, biodiversity, and local livelihoods in current ecosystems. However, the spatially heterogeneous and environmentally sensitive dryland can also make the highly vulnerable to climate change and human disturbance. The biophysical structure and ecological processes can fundamentally shape their functional integrity, which in turn can influence both global environmental and socioeconomic change. Fortunately, the large-scale desertification control programs have been launched to meet the national requirements for the ecological civilization and high-quality development, such as the "Three-North Shelterbelt" Program. Significant progress has been achieved to improve the vegetation cover and restore the degraded land. However, it is still challenging to assess the adaptive capacity, functional stability, and resilience of these ecosystems under growing climate variability and human pressure. In this study, an integrated monitoring and adaptive governance framework was developed to synergistically improve the ecological function and stability in dryland desert ecosystems. A systematic review was also conducted on the recent classification of the desert ecosystem, structural-functional dynamics, nonlinear behaviors, critical thresholds, and adaptive governance. Research gaps were then identified, including thematic fragmentation, static assessments, and oversimplified interpretations of complex interactions. The nonlinear ecological and multi-factor synergies were achieved to enhance the scientific findings. Four themes were selected in the research framework: remote sensing monitoring of desert ecosystems, mechanistic analysis of ecosystem evolution, identification of nonlinear thresholds, and multi-scenario adaptive governance for the function and stability enhancement. Some approaches were incorporated into the framework, such as the coordinated “space-air-ground-web” observation network, spectral mixture analysis for the fine-scale component extraction, nested ecosystem typology mapping, and multi-functional metric development. It also included the nonlinear time-series analysis, mechanism modeling, structure–function state diagnosis, tipping point detection, dynamic zoning, stability assessment, multi-scenario simulation, and adaptive strategy. Together, these techniques were utilized to clarify the evolutionary mechanisms of the landscape patterns, key structures, and functional attributes in dryland ecosystems. Accurate identification of the critical thresholds was also realized after optimization. An early-warning system was integrated to classify the ecosystem states into the “healthy,” “transitional,” and “degraded” categories, thus supporting the targeted monitoring. The reflexive and learning-enabled governance was allowed for the continual adaptation using real-time monitoring and predictions. There was also the shift from static and uniform management toward dynamic, context-sensitive, and resilient governance in drylands. This approach can offer a scientific basis to develop the region-specific pathways for high stability and adaptive governance. This finding can also provide a scalable and transferable framework in order to improve the effectiveness of the ecological restoration for the national ecological security. The insights are also relevant for the arid land worldwide, especially in the context of environmental uncertainty and global climate.