Forest litterfall links aboveground vegetation dynamics with belowground biogeochemical processes and plays an important role in soil microclimate regulation, erosion protection, and wildfire risk assessment. However, long-term, fine-resolution maps of litterfall production remain limited. Here, we compiled litterfall observations from the Chinese Ecosystem Research Network and published literature, and developed a spatial matching scheme to account for coordinate uncertainty in linking field measurements with remote sensing predictors. We integrated Landsat-derived spectral features, climatic variables, and topographic factors into a Random Forest model to generate annual 30 m forest litterfall production maps for China from 2000 to 2024. The model performed well for independent testing samples, with an R² of 0.72, and the resulting 30 m product captured finer spatial heterogeneity than coarser-resolution products, particularly in fragmented forests and along forest edges. In 2024, mean forest litterfall production across China was 397 g m<sup>-2</sup>, with higher values generally found at lower latitudes and in evergreen forests. From 2000 to 2024, litterfall production increased across approximately 75 % of China's forest areas, leading to an overall increase in national total litterfall input. Precipitation showed the strongest association with interannual litterfall variations, particularly in northeastern China, whereas temperature-related associations were more pronounced in southern and northwestern China. These results provide new spatial evidence for understanding forest litterfall dynamics and support improved representation of litter inputs in carbon and nutrient cycling assessments.