Modern AI accelerators provide high-throughput low-precision matrix engines, but their support for FP32 GEMM is often limited or inefficient. This work presents SGEMM-cube, a precision-recovery FP32 GEMM approximation on Ascend NPUs using FP16 Cube units. Rather than claiming bit-exact FP32 approximation, SGEMM-cube targets near-FP32 accuracy for inputs whose magnitudes are representable within the FP16 dynamic range. The method follows a two-component FP32-to-FP16 splitting strategy related to Ozaki-style and Ootomo-style schemes: each FP32 operand is represented by an FP16 high component and a scaled FP16 residual component, and the matrix product is reconstructed from the dominant high-high and high-low terms while omitting the low-low term. The main contribution of this paper is not a new splitting paradigm, but an architecture-specific realization and analysis of this precision-recovery scheme on Ascend NPUs. We analyze the effects of round-to-nearest conversion, underflow, residual scaling, and accumulation order under the Ascend execution model, and clarify the range and accuracy limitations of the approach. We further adapt standard high-performance GEMM techniques, including L1-aware blocking and double-buffered pipelining, to the software-managed memory hierarchy of Ascend NPUs. Experiments on Ascend 910A show that SGEMM-cube recovers substantially higher accuracy than native FP16 GEMM and approaches FP32 SGEMM accuracy for moderate-range inputs, while achieving up to 65.3 TFLOP/s, corresponding to 77\% of the FP32-equivalent peak defined by the three-GEMM decomposition cost. These results demonstrate that FP32-accuracy GEMM approximation can be made practical on FP16-only NPU matrix engines, provided that its range, error, and implementation constraints are explicitly managed.