Abstract: Thermal anomalies over the Qinghai−Xizang Plateau (QXP) play a critical role in driving the Asian monsoon system and global atmospheric circulation. However, the selection of heat source data for the QXP remains controversial in climate impact research. Therefore, evaluating the suitability of multisource datasets for the QXP is of particular importance. Based on station observations and reanalysis datasets (JRA-55, ERA5, and MERRA2), this study systematically analyzed the multiscale characteristics of the springtime surface sensible heat (SH) enhancement process in the central and eastern Qinghai−Xizang Plateau (CEQXP) from 1982 to 2020 and evaluated the applicability of multisource daily surface SH flux data in this region. The results show that (1) on average, the transition time (T1), when the surface SH over the CEQXP shifts from weak to strong within a year, occurs during the 14th–17th pentads (early and mid-March). Subsequently, SH continues to increase. The peak time (T2) consistently occurs before the 30th pentad. The average duration of the increasing phase in spring is approximately 14 pentads (approximately 70 days), and the magnitude of the increase (Q) ranges from 45 to 60 W m⁻². In terms of intraseasonal variability, SH enhancement exhibits a pronounced quasi-biweekly oscillation with a dominant periodicity of 10–20 days, accounting for 30%–50% of the total seasonal variance. Regarding phase evolution, the pattern is primarily characterized by a monopole mode. T1 and T2 exhibit a trend shift at around 2000, characterized by an initial decrease followed by an increase. Meanwhile, Q displays a notable weakening in climatological state around the same period. (2) Evaluation of three surface SH flux reanalysis datasets reveals that, compared with JRA-55 and ERA5, MERRA2 exhibits significantly higher correlations with station observations in intensity and phase evolution of quasi-biweekly oscillations during the spring SH enhancement process and interannual variations of T1, T2, and Q. This indicates that MERRA2 has the best applicability over the CEQXP, followed by JRA-55, whereas ERA5 performs relatively poorly. (3) Evaluation of spring surface wind speed and surface–air temperature difference increments shows that all three datasets reasonably capture the interannual variability and spatial distribution of surface wind speed, with the reanalysis data exhibiting significant correlations with observations at more than 50% of stations. However, ERA5 underestimates the mean value of the interannual series by approximately 50%over the QXP, whereas JRA-55 and MERRA2 show anomalously large values over the southwestern QXP. For the temperature difference increment, MERRA2 shows the closest spatial distribution to station observations, whereas JRA-55 and ERA5 show climatologically underestimated and overestimated values, respectively, due to inconsistent deviations in surface and air temperature increments. Overall, MERRA2 outperforms the other two datasets, which may explain its superior performance in capturing intraseasonal and interannual variations of the surface SH enhancement process. These findings provide a valuable reference for selecting daily SH flux data over the QXP and lay the foundation for a more comprehensive understanding of the climatic impacts of anomalies in the spring surface SH enhancement process in the QXP.