Abstract Accurate cloud base height (CBH) over the Tibetan Plateau—Earth's Third Pole—is essential for monsoon dynamics, glacial melt, and water security, yet ERA5 systematically underestimates it. Here, we present a two‐step machine learning framework to mitigate this hidden bias. Step 1 refines the ERA5 diagnostic informed by 3 years of ground‐based Lidar observations (October 2021–December 2024), reducing the site‐level MBE from 1.8 to 0.1 km and improving the regional correlation with CALIPSO from 0.25 to 0.40. Step 2 applies an Optuna‐optimized XGBoost model trained on high‐confidence CALIPSO observations, fusing the refined ERA5 data with vertical atmospheric profiles and surface attributes. The final product achieved a test‐set RMSE of 1.87 km ( R 2 = 0.71, MBE = −0.02 km), with seasonal correlations reaching 0.72–0.86 and southern plateau bias reduced to −0.11 km, a 97.9% improvement. This approach enables reliable, long‐term CBH reconstruction, key to better climate modeling and water assessments across High Mountain Asia.
Zhao et al. (Tue,) studied this question.