Recent decades have seen a marked climatic wetting across north-western China’s semi-arid transition zone, yet the extent to which this intensification arises from local land–atmosphere feedback or from external moisture inflow remains uncertain. Using hourly station observations and ERA5 reanalysis for 2020–2024, we develop a process-based framework that links event-scale rainfall variability to the dynamic behaviour of precipitation recycling. Hourly recycling rates are derived through a two-reservoir moisture-tracking scheme, and multi-day wet episodes are identified to isolate transient feedback processes. Machine-learning surrogate models (CatBoost, XGBoost, ExtraTrees, Gradient Boosting) emulate the recycling rate as a function of meteorological conditions under a leave-one-event-out design, enabling counterfactual perturbation experiments in which precipitation intensity and key moisture variables are systematically scaled. A dimensionless percentage elasticity (<em>η%</em>) is introduced to quantify the relative response of recycled precipitation to rainfall enhancement. Results from nine regional events reveal a robustly negative<em> η%</em>, indicating that additional rainfall often suppresses rather than reinforces local recycling efficiency—a self-limiting wetting feedback. Cluster analysis distinguishes three physical regimes: (1) cool–moist episodes with initially strong but rapidly saturating coupling, (2) advection-dominated events with nearly linear, externally controlled responses, and (3) warm–dry episodes with weak coupling and near-zero elasticity. Collectively, these findings depict the atmosphere above north-western China as a self-stabilizing hydrological system in which increased precipitation does not necessarily strengthen, and may even weaken, local moisture recycling. The proposed event-scale elasticity framework provides a transferable diagnostic for short-term land–atmosphere coupling and for assessing hydrological resilience in arid and semi-arid regions.