Mitigation Drivers of China’s Electricity Grid Revealed by Monthly Variability of Carbon Emission Factors

China needs rapid electricity system decarbonization to achieve climate goals, yet planning still relies on annual average grid emission factors (GEFs) that ignore time variation. In this study, we construct monthly electricity networks in China from January 2019 to December 2023 using operational grid data and then apply a multiseasonal structural decomposition of emission changes to quantify driver contributions. We find pronounced intra-annual variations in GEFs: within provinces, the month-to-month variability reaches a standard deviation of up to 0.25 kg CO₂ /kWh, and the interprovincial dispersion reaches 0.14 kg CO₂ /kWh. Using annual-average provincial GEFs of electricity consumption, which ignore monthly variability in GEFs and electricity demand, results in a national overestimation of aggregated emissions, particularly in October, and conceals seasonal decarbonization signals. The decomposition shows mitigation drivers shifting from a transmission structure to the carbon intensity of electricity transfers, especially in the autumn. Incorporating monthly GEFs into annual carbon target setting and seasonal management could inform flexible system planning and mitigation strategies, facilitating demand-side management and carbon capture, utilization, and storage deployment, and advancing electricity system decarbonization.