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Solar power generation is an effective way to reduce carbon emissions and has a wide range of applications worldwide. China's newly installed photovoltaic capacity has ranked first in the world in recent years. Timely and accurate monitoring of the spatiotemporal distribution characteristics of solar power plants is essential to optimize China's renewable energy power distribution and achieve carbon reduction targets. However, long-term solar panel (SP) datasets are still lacking. Based on the Google Earth Engine platform, this study proposed a fine extraction method framework of SPs in large and complex geographical environments by integrating stratified sampling and zonal modeling and obtained the first comprehensive dataset of SP distribution in China covering 2000–2022 to fill the gap in relevant research and practice. It has been verified that the F1-score of the SP datasets was higher than 0.87, which could meet the research needs. On this basis, this study revealed the spatiotemporal variation and development trends of SPs in China over the past 20 years. The proposed method framework can serve as a scientific reference for relevant research, while the interpreted SP datasets and analysis results can provide a basis for regional decision making and related research in the context of future climate change.