Long-term consistent records of Total Column Ozone (TCO) are of great significance for assessing ozone layer changes and continuous observation. Although there is abundant satellite monitoring data for ozone, the consistency between different datasets is poor. Differences in satellite payloads, design calibration of spectrometers, and inversion algorithms lead to significant cross-payload biases in TCO observations in the same region. To obtain consistent TCO records, homogenization at the raw data and algorithms level is more physically meaningful, but it requires complete sharing of all instrument parameters, raw data, and all inversion algorithms between different satellite payload teams, which is very difficult. This paper introduces a method to eliminate cross-payload systematic bias based on statistics. In this paper, a quantile-quantile (Q-Q) bias correction method is proposed to eliminate the cross-payload TCO systematic bias between the Environmental Trace Gases Monitoring Instrument 2 (EMI-2) and the TROPO spheric Monitoring Instrument (TROPOMI). Using the overlapping observations in November 2021, this study characterizes the systematic bias between EMI-2 and TROPOMI through the Q-Q bias correction method. Then, it homogenizes the TCO observations of EMI-2 in December 2021 to the TROPOMI level. This Q-Q bias correction method significantly improves the overall consistency of cross-payload TCO observations, increasing the correlation coefficient R between EMI-2 and TROPOMI from 0.96 to 0.98, providing a basis for continuous ozone observation. Bias analysis of the data before and after homogenization of EMI-2 with ground station data shows that the Q-Q bias correction method improves the accuracy and consistency of EMI-2 observations, reducing the error with ground-based data from 5% to 3%. Ground station data indicate that the accuracy of EMI-2 data is higher in temperate and polar regions, but the error is higher than 5% in tropical regions. It is preliminarily speculated that this is because the cloud height is higher. The cloud fraction is larger in tropical regions, and the accuracy of cloud pressure and cloud fraction in cloud data is insufficient. The effect of compensating for the ozone below the clouds with “ghost columns” is poor, but the bias is reduced after homogenization. The study shows that the Q-Q bias correction method introduced in this paper is crucial for global long-term TCO records and can be applied to future assessments of global ozone recovery.