Investigating the characteristics of wind speed holds immense significance within meteorology and various applied sciences. The wind energy industry is experiencing a period of expansion and progress, offering opportunities for establishing offshore wind farms and expanding current land-based wind farms. To comprehend the fundamental aspects of the wind field for the expansion of wind farms, it is crucial to possess reliable long-term data on wind speed. Reanalysis datasets are one of the primary sources for long-term consistent gridded datasets; however, these datasets are fallible to uncertainties. Thus, the reliability of such products is necessary before assessing the potential for wind farm expansion. This study compares daily 10 m wind speed (m/s) from three-reanalysis products (ERA5, JRA55, and NCEP) and their ensemble mean with HadISD wind observations based on the 99% threshold based on the valid dataset available for South Asia during 1974–2004. Statistical metrics such as standard deviation (STD), mean absolute error (MAE), percent bias (PBIAS), and correlation coefficient (CC) are applied to the assessment of the reanalysis products. Results demonstrated that the JRA55 reanalysis product demonstrates the highest level of accuracy among the other products. However, significant disparities were detected between the simulated wind speeds and the actual observations. The JRA55 reanalysis product accurately reflected the changes seen in the Subcontinent observations and was subsequently succeeded by the ERA5 product. The results estimations of JRA55 show exceptional agreement with wind speed observations, as evidenced by its high CC of 0.676, a PBIAS of 21.543, and a low MAE of 1.014. On the other hand, when comparing it to JRA55, ERA5 shows larger MAE and PBIAS, and lower CC. Compared to JRA55 and ERA5, NCEP exhibited a tendency to overestimate, deviating from the expected values. Based on this comparison, this research seeks to assess and distinguish the reliability of reanalysis products when compared to the observed dataset, emphasizing the importance of exercising caution when relying on reanalysis data to evaluate and predict winds in the South Asia region.