The spring predictability barrier (SPB) of the El Ni?o–Southern Oscillation (ENSO) is a difficult problem in ENSO prediction. To understand how dynamic and thermal factors affect the variability of sea surface temperature (SST) over the tropical Pacific ocean during spring is very important in understanding SST changes in key areas and resolving the SPB problem. In this work, a set of monthly data, including sea surface wind stresses, sensible heat flux, latent heat flux, net longwave radiation, net shortwave radiation, and ocean current fields, coordinated with each other during 1986–2017, were generated by BCC-CSM2-MR model simulation. Based on these data, we analyzed the dynamic and thermal influences and their contributions to the variability of SST (hereafter, T_S). The main results were as follows: (1) Compared with other seasons, in spring, T_S presented a unique asymmetric seasonal shift from warming to cooling in the Ni?o3.4 area. This was due to a similar shift in wind stress, net energy fluxes, and ocean current, which had a robust relationship with T_S. Further analyzes demonstrated that thermal effects play an important role in the variability of local T_S. In contrast, Horizontal advection is dominated by negative contribution. Meridional advection always showed a negative contribution to the seasonal variability of T_S. Meanwhile, zonal advection terms turned into a cooling effect from a warming effect on T_S during spring, and vertical advection effect was quite weak. (2) The interannual correlation between the tendency in T_S anomaly and dynamic/thermal effects showed that thermal heating, as well as zonal advection, were positively associated with the Ni?o3.4 T_S anomaly in spring. However, the correlation between meridional advection and the T_S anomaly changed from negative to positive during spring. (3) Quantitative analysis of the dynamic and thermal variance contributions in the Ni?o3.4 region also suggested that the contribution rate of thermal effect was ＞50%, and the corresponding correlation coefficient was ＞0.7. The contributions of zonal and meridional advection were about 10% and 20%, respectively, but were inverse to each other. Other factors contributed less.