Lake water clarity, quantified as Secchi disk depth (SDD), serves as a useful indicator of aquatic ecosystem health and sensitive proxy for environmental shifts. As the MODIS mission nearly ends, maintaining long-term, high-frequency monitoring of inland waters becomes an urgent Earth observation challenge. Current alternatives face trade-offs between spatial resolution and temporal frequency. Addressing this critical observational gap, this study presents a lake water clarity dataset for China derived from VIIRS 375-m observations (CLWC-375), the first national-scale inland water clarity dataset (2012&ndash;2025) for 767 lakes larger than 10 km² across China, derived from the high-resolution VIIRS 375-m imagery band (I-band).</p> <p>Our methodological framework utilizes a robust semi-analytical empirical model optimized for the remote sensing reflectance (<em>R</em>_rs(<em>&lambda;</em>)) of the VIIRS Image-band (I1). Extensive independent validation against 954 in situ measurements from 74 lakes across a broad optical range (0.1 m to 16.0 m) demonstrates good retrieval accuracy. The customized regional algorithm achieves a high coefficient of determination (<em>R</em>² = 0.85) and uncertainty of 29.5 %, effectively eliminates systematic overestimation in existing global operational VIIRS products. The resulting 14-year, high-frequency dataset captures dynamic aquatic processes and reveals spatiotemporal trajectories. Morphology is related to optical properties: deep plateau lakes consistently have high clarity, whereas shallow lowland lakes have considerable turbidity. Chinese lakes had a significant overall clearing trend (0.28 m decade^-1) since 2012, driven predominantly by rapid improvements in the Tibetan and Yunnan-Guizhou plateaus. By successfully overcoming the spatial-temporal resolution trade-off, the CLWC-375 dataset provides a sustainable observational baseline for decoupling the complex optical responses of inland waters to intense anthropogenic activities and global climate change in the post-MODIS era. The dataset is publicly available at the ScienceDB repository (<a href="https://doi.org/10.57760/sciencedb.31441" target="_blank" rel="noopener">https://doi.org/10.57760/sciencedb.31441</a>).