Aerosol Optical Depth (AOD) is a key parameter for characterizing the radiative forcing of atmospheric aerosols, yet some inconsistencies remain among different satellite products. In this study, Level-2 (L2) instantaneous and Level-3 (L3) monthly AOD products from Visible Infrared Imaging Radiometer Suite (VIIRS), Moderate Resolution Imaging Spectroradiometer (MODIS), and Multi-angle Imaging Spectroradiometer (MISR) were evaluated using observations from eight AERONET sites across East Asia for the period 2012-2023. We further analyzed the spatial distribution patterns and long-term climatological characteristics of AOD in this region. The results show that VIIRS generally exhibits the best overall performance for both L2 and L3 AOD products relative to AERONET, characterized by high correlation coefficients (R = 0.853 and 0.881), large fractions of retrievals within the expected error (75.34 % and 70.27 %), and comparatively low root mean square error (RMSE) and mean bias error (MBE). Nevertheless, the retrieval accuracy still exhibits pronounced regional variations among these satellite products. In desert regions, MISR performs the best with the highest fractions of retrievals within the expected error (65.14 % and 72.86 %), followed by MODIS, while the VIIRS L3 product shows notable overestimation. The consistency between L2 instantaneous retrievals and L3 monthly aggregates also varies considerably across surface types and among the three sensors. Despite these differences, the three satellite products display broadly consistent spatial patterns and temporal evolutions, commonly identifying similar highAOD regions. On the interannual scale, all products reveal a persistent decline in AOD from central to eastern China (VIIRS: -0.021 a- 1, MODIS: -0.023 a- 1, MISR: -0.013 a- 1) and an increasing trend over the Indian subcontinent (VIIRS: 0.006 a- 1, MODIS: 0.008 a- 1, MISR: 0.004 a- 1). To further elucidate the climatological behavior of AOD and identify the meteorological factors most strongly associated with its long-term variations, a method combining principal component analysis (PCA) and multiple linear regression (MLR) is applied. The results indicate that over land, the AOD climatology and its interannual variability are most strongly associated with boundary layer height, whereas over ocean, AOD is influenced by a more complex combination of meteorological factors.