Recent advancements in computational fluid dynamics(CFD) driven by the heterogeneous computing techniques and high-fidelity numerical methods have significantly increased the demand for efficient input/output(I/O) operations. In GPU-accelerated CFD, redundant data copies and excessive CPU overhead have become prominent challenges for efficient IO due to increasing complexities in data transfers between memory and storage. In this work, we propose a GPU native I/O framework(named as GPUDirectIO) for high-performance CFD by redesigning the Data Mapping Layer(DML) and data structures of the CFD General Notation System(CGNS), which is a widely used file format for complex CFD applications. This GPU-centric system enables direct data transfers between NVMe storage and GPU memory via GPU Direct Storage(GDS), effectively streamlining heterogeneous CFD I/O workflows. To further improve I/O throughput, we develop a CGNS-based distributed data management strategy that leverages an NVMe storage array to fully utilize the GPU bandwidth. We compare the performance of the proposed GPUDirectIO with existing CPU-mediated I/O approaches with different number of threads using CFD datasets where the maximum number of computa tional grid points reaches 1.6 billion. The results demonstrate the superiority of GPUDirectIO. Single-threaded GPUDirectIO achieves read and write rates 2.95× and 3.49× those of CPU mediated I/O, respectively, and reduces transmission latency by approximately 59%. When applied to distributed storage systems, multi-threaded GPUDirectIO shows read and write rates 3.23× and 4.68× those of CPU-mediated parallel I/O, respectively, with transfer latency reduced by about 39%. GPUDirectIO has also demonstrated excellent parallel efficiency and speedup ratios in both strong and weak scalability tests.