The growing adoption of Go for concurrent programming highlights its strengths in performance and simplicity, yet its unique concurrency model—combining shared memory and channel-based communication—introduces significant challenges for data race detection. Existing dynamic analysis tools suffer from high false negatives due to path-dependent execution, while static approaches often fail to account for Go-specific features like channels and context propagation, leading to excessive false positives. To address these limitations, we present GRace, a static analysis framework tailored for Go that systematically models concurrency semantics to improve detection accuracy. GRace first identifies shared memory locations through pointer analysis and syntax-aware rules, then applies lock set analysis to filter protected accesses. By constructing a context-sensitive happens-before graph that integrates Go’s synchronization mechanisms (e.g., channels, WaitGroups, Context cancellation), GRace refines event ordering and computes vector clocks to eliminate ordered accesses. Evaluated on real-world projects and benchmark datasets, GRace detected 18 data races in open-source repositories, with 9 confirmed and 7 fixed by developers.