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In this study, the ability of the Weather Research and Forecasting (WRF) model to generate accurate near-surface wind speed forecasts at kilometer- to subkilometer-scale resolution along race tracks (RTs) in Chongli during the wintertime is evaluated. The performance of two postprocessing methods, including the decaying-averaging (DA) and analogy-based (AN) methods, is tested to calibrate the near-surface wind speed forecasts. It is found that great uncertainties exist in the model's raw forecasts of the near-surface wind speed in Chongli. Improvement of the forecast accuracy due to refinement of the horizontal resolution from kilometer to subkilometer scale is limited and not systematic. The RT sites tend to have large bias and centered root mean square error (CRMSE) values and also exhibit notable underestimation of high-wind speeds, notable overestimation or underestimation of the near-surface wind speed at high altitudes, and notable underestimation during daytime. These problems are not resolved by increasing the horizontal resolution and are even exacerbated, which leads to great challenges in the accurate forecasting of the near-surface wind speed in the competition areas in Chongli. The application of postprocessing methods can greatly improve the forecast accuracy of near-surface wind speed. Both methods used in this study have comparable abilities in reducing the (positive or negative) bias, while the AN method is also capable of decreasing the random error reflected by CRMSE. In particular, the large biases for high-wind speeds, wind speeds at high-altitude stations, and wind speeds during the daytime at RT stations can be evidently reduced.