Abstract: In the context of intensified global climate change and the accelerating transition of energy systems, wind power—as a clean and low-carbon renewable energy source—has become an important component of modern power systems. Accurate wind speed forecasting is vital for predicting wind power, grid dispatching, and stably operating wind farms. Therefore, improving the accuracy of wind speed predictions is crucial for enhancing the efficiency of wind energy utilization and supporting the safe integration of large-scale wind power into the power grid. However, because of the inherent intermittency, randomness, and nonlinearity of wind speed, achieving accurate and stable forecasts remains challenging. Numerical weather prediction (NWP) models, such as the Weather Research and Forecasting (WRF) model, are widely used for wind speed forecasting. However, their predictions typically contain systematic biases and structural errors caused by imperfect initial conditions, physical parameterization schemes, and complex terrain effects. In this study, a wind farm located in Gansu Province, China, is selected as the research object. Based on the WRF NWP model, short-term wind speed forecasts are conducted, and a segmented hybrid correction framework is proposed to improve the prediction accuracy. Specifically, a Segmented Error-aware Hybrid Correction (SEHC) framework is developed to correct the wind speed predictions by the WRF model using a wind-speed-segmented structure. Within this framework, three complementary correction methods—probability density function (PDF) matching, gated recurrent unit (GRU), and the k-nearest neighbors (KNN) algorithm—are integrated to capture different types of prediction errors. While PDF matching is used to reduce systematic distribution bias, the GRU model is employed to capture temporal dependencies in wind speed sequences, and the KNN algorithm is utilized to address local nonlinear relationships in the data. By combining these methods within different wind speed segments based on the dominant error characteristics, the proposed SEHC framework aims to achieve a more robust and accurate wind speed correction. The model was trained and evaluated using observational wind speed data and WRF forecast data with a temporal resolution of 15 min. The dataset encompassed a full year of observations from the selected wind farm. The performance of the proposed correction framework was evaluated using several statistical indicators, including mean absolute error (MAE), root mean square error (RMSE), correlation coefficient, and distribution-based metrics. The results showed that the proposed SEHC framework significantly improved the accuracy of wind speed predictions compared with the original WRF model outputs. In particular, the MAE was reduced by approximately 80%. In addition, the dispersion of the boxplot distribution and the tail quantiles of prediction errors were reduced significantly after correction. The most significant improvement was observed in the wind speed range of 3–10 m·s⁻¹, which corresponded to the region where the power output of the wind turbine increased rapidly along the power curve. Furthermore, the proposed method effectively suppressed extreme prediction deviations under high-wind conditions and thereby reduced the potential risks associated with large forecast errors. In general, the proposed SEHC framework demonstrated a strong capability for improving the accuracy of wind speed predictions without modifying the physical structure of the NWP model. The framework provides a practical and computationally efficient solution for wind speed post-processing in wind farm applications. The results of this study provide useful technical support for refined wind speed forecasting and offer valuable references for predicting wind power, grid dispatching, and the operational management of wind farms.