Improving the resilience of wastewater treatment facilities (WWTFs) has never been more important with rising risks of disasters under climate change. Beyond physical damages, non-physical shocks induced by disasters warrant attention. Human mobility is a vital mediator in transferring the stresses from extreme events into tangible challenges for urban sewage systems by reshaping influent characteristics. However, the impact path remains inadequately explored. Leveraging the stay-at-home orders during the COVID-19 pandemic as a natural experiment, this study aims to quantify and interpret the heterogeneous impacts of mobility reduction on the influent characteristics of WWTFs with different socio-economic, infrastructural, and climatic conditions. To achieve this goal, we developed a research framework integrating causal inference and interpretable machine learning techniques. Based on the empirical data from China, we find that 79.1% of the studied WWTFs, typically located in cities with well-developed drainage infrastructures and low per capita water usage, exhibited resilience against drastic mobility reduction. In contrast, 20.9% of the studied WWTFs displayed significant variations in influent characteristics. Large-capacity WWTFs in subtropical regions encountered challenges with low-load operations, and small-capacity facilities in suburban areas grappled with nutrient imbalances. This study provides valuable insights to equip WWTFs in anticipating and adapting potential variations in influent characteristics triggered by mobility reduction.