 This study proposes a novel spatiotemporal fitting algorithm to gap-fill the Daily Modi's aerosol optical depth, AOD, product, which is suitable for large area application with high efficiency to obtain Gapless AOD with reasonable spatial pattern and complete coverage. The algorithm is a multi-stage method aimed to address the non-stationary nature of AOD time series. The proposed algorithm outperforms the interpolation-based correlation weighting, ICW, and inverse distance weighting, IDW, algorithms in accuracy and meets the need of typical applications in relevant studies. The algorithm is transferable to other regions with the potential to be used even operationally and efficiently for generating accurate Gapless Global Daily AOD datasets with the input of only Modi's Maya KOD data. This article was authored by Dao Zhong, Yu Yu Zhu, Kai Guang Zhao, and others.