This paper carried out CO₂ inversion experiments based on the remote sensing data from the greenhouse gases monitoring instrument (GMI) on the GF-5 satellite in China, calculated the CO₂ profile samples according to the differences in China’s regional characteristics, and constructed the representative sample set suitable for China’s regional characteristics. Then, it substituted the CO₂ profile obtained by statistical inversion as the initial value into the physical inversion method to form a new algorithm for synergistic statistics and physical methods. By analyzing the inversion results of the new algorithm, we conclude that the collaborative inversion algorithm improves the accuracy by 47.7% on the basis of using the physical inversion algorithm alone, and the correlation between the inversion results of the new algorithm and the observation results provided by the international satellite of the same type, OCO-2, reaches 88.5%.