Land surface temperature (LST) is an essential climate variable in geophysical, ecological, and environmental researches. Remote sensing provides a unique observation approach for obtaining large-scale LST products. However, current official LST datasets (such as FY-4A) are limited by the unaddressed thermal radiation directionality effect, and suffer the spatial discontinuities due to the pervasive presence of clouds. What’s more, the geostationary LST products have relatively coarser resolution than those of polar-orbiting satellites due to trade-off between spatial and temporal resolutions. Based on the official hourly FY-4A LST dataset, this study proposes a novel framework for generating angular-normalized, cloud-filled, and 0.01°-downscaled LST (ANCFDS-LST) product, encompassing directional (<em>T<sub>dir</sub></em>), nadir (<em>T<sub>nadir</sub></em>), and hemispherical (<em>T<sub>hemi</sub></em>) LST layers. First, the angular-normalized <em>T<sub>nadir</sub></em> and <em>T<sub>hemi</sub></em> were generated using a time-evolving kernel driven model (TEKDM) with the inputs of multi-temporal FY-4A <em>T<sub>dir</sub></em>. Subsequently, hypothetical clear-sky LST were predicted using a CatBoost model optimized via Bayesian methods. The cloudy-sky LST values were then derived through a cloud radiation force (CRF) correction. Finally, the 0.05° all-weather <em>T<sub>dir</sub></em>, <em>T<sub>nadir</sub></em>, and <em>T<sub>hemi</sub></em> values were downscaled to 0.01° resolution using an improved hybrid downscaling algorithm (IHDA) combining fusion and kernel-based methods. Taking the daytime clear-sky near-nadir VNP21A1 LST as reference, the 0.05° <em>T<sub>dir</sub></em> before angular-normalization has a root mean squared difference (RMSD) of 6.21 K and a mean bias difference (MBD) of -4.04 K, whereas the angularly normalized <em>T<sub>nadir</sub></em> has a much smaller RMSD of 3.48 K and a better MBD of -2.13 K. For the all-weather <em>T<sub>hemi</sub></em>, temperature-based validation over 15 sites in the Heihe River Basin and the Tibetan Plateau shows a root mean squared error (RMSE) and mean bias error (MBE) of 2.99 K and -0.77 K under clear-sky conditions, 4.56 K and -1.56 K under cloudy-sky conditions. After the spatial downscaling, the 0.01° all-weather <em>T<sub>hemi</sub></em> with abundant texture details exhibits an RMSE (MBE) of 3.99 K (-1.32 K) over 15 sites. The generated LST products from 2018 to 2023 over the FY-4A disk exhibit enhanced angular consistency, spatial continuity, and finer resolution, offering valuable support for subsequent LST-related applications. The ANCFDS-LST data is freely available at <a href="https://doi.org/10.11888/RemoteSen.tpdc.303249" target="_blank" rel="noopener">https://doi.org/10.11888/RemoteSen.tpdc.303249</a> (last access: 30 January 2026; Na et al., 2026).