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Greenhouse gas monitoring satellites provide extensive observational data for the global remote sensing of atmospheric carbon dioxide (CO₂), yet a critical limitation in utilizing these data is the dependence of the full physics retrieval accuracy on a priori CO₂ profiles. This challenge is pronounced due to the significant time delay inherent in data assimilation products of high quality, whose latency prevents their use for retrieval in real time. The resulting temporal mismatch between the a priori constraint and the actual atmospheric state is a primary source of systematic bias in the retrieved CO₂. To address this issue, this paper develops a random forest-based CO₂ profile emulator (RF-CPE) with the core novelty of emulating the high-quality Carbon Tracker CO₂ profiles in real time. By learning the complex relationships between multisource features and the corresponding Carbon Tracker profiles, the emulator generates a dynamic profile specific to each observation. The application of this emulator-based approach to TanSat observations from 2017 to 2018 demonstrates significant performance gains, reducing the mean retrieval bias by 44.11% (from 2.63 ppm to 1.47 ppm) compared to using a static prior. The emulator itself exhibits high performance, with an R² of 0.71 and an RMSE of 2.13 ppm, in agreement with the Carbon Tracker data. Ultimately, this work presents a robust and computationally efficient solution that resolves the conflict between the accuracy and timeliness of a priori constraints, effectively translating the performance of a delayed assimilation system into a real-time retrieval framework to significantly enhance the reliability of satellite CO₂ monitoring. Full article