Urban building clusters can significantly alter the low-altitude urban wind field, creating local regions with high wind speed, intense turbulence, and strong non-uniform disturbances. These conditions may cause lateral deviation, attitude instability, increased trajectory error, and even exceed the wind-resistance limits of low-altitude UAVs. To address the lack of explicit modeling of local high-risk wind regions in traditional path planning methods, this paper proposes a wind-risk-aware path planning method, termed RP- A* (Wind Risk-Aware Probability A*). First, a steady-state RANS urban airflow model with the standard k-ε turbulence model is used to obtain the mean wind field and turbulence statistics in the low-altitude flight region. Second, the CFD results are reconstructed on a grid to build a wind risk model consisting of gust risk, crosswind risk, and overall wind-limit-exceedance risk. Third, a direction-dependent integrated risk cost is introduced into the A* search framework, and the RP-A* algorithm is developed to achieve path planning that balances route efficiency and flight safety. Finally, Monte Carlo simulations driven by turbulence-based wind perturbation samples are conducted to estimate the empirical failure rate of planned paths. Results show that RP-A* significantly reduces path failure risk compared with baseline shortest-path methods while requiring only a limited increase in path length. The proposed framework provides an effective approach for safe UAV path planning in complex urban low-altitude environments.