In response to frequent changes in regulatory rules, this paper proposes CARE, a cascading impact-aware framework for automated compliance testing evolution. Existing approaches often suffer from over-reuse or missed updates because they treat rule changes in isolation and ignore complex interdependencies across testing artifacts. CARE addresses this issue by constructing a unified four-layer cascading relation model spanning Rule-Requirement-Scenario-Test Case, enabling fine-grained traceability across abstraction levels. By explicitly modeling how rule changes propagate and amplify along this chain, the framework precisely identifies impacted scenarios and test cases that need updating, while safely maximizing the reuse of unaffected ones. Experiments conducted on real-world compliance testing tasks across multiple domains show that CARE achieves an average F1 of 90.3% on updated test suites, outperforming existing methods by up to 164% and approaching expert-level effectiveness. Ablation studies further demonstrate that explicit cascading impact modeling and handling are key contributors to these improvements. In addition, CARE substantially reduces manual effort and improves test maintenance efficiency, and indicates strong cross-domain generalization. This work highlights cascading impact propagation as a central challenge in regulation-driven test maintenance and shows that shifting from isolated rule handling to cascading impact-aware evolution is essential for achieving both high test quality and maintenance efficiency.