Abstract:Abstract: This study evaluates the performance of the China Meteorological Administration Land Data Assimilation System (CLDAS) 5-km hourly fused analysis products by comparing them with ground meteorological station observations, which located in Urumqi City and Changji Hui Autonomous Prefecture region from January to December 2023. The methodology employs bilinear interpolation for spatial matching and calculates statistical metrics, including the correlation coefficient (COR), mean error (ME), mean absolute error (MAE), mean relative error (MRE), and root mean square error (RMSE). Spatial heterogeneity of errors in temperature, relative humidity, and wind speed products is further analyzed using the Moran’s I index and local indicators of spatial association (LISA) cluster analysis. The key findings are as follows: ⑴ The diurnal variations of CLDAS temperature, relative humidity, and wind speed align closely with station observations, indicating strong capability in capturing spatiotemporal patterns under synoptic-scale weather conditions. ⑵ On an hourly scale, temperature (COR > 0.95) and relative humidity (COR > 0.88) exhibit high consistency with observations, whereas wind speed shows weaker correlations (COR = 0.65–0.83), particularly in southern mountainous areas where terrain-induced parameterization limitations result in significant errors (RMSE ≥ 1.56 m/s). ⑶ While temperature products demonstrate reliability in plains (MAE ≤ 0.76°C), higher independent validation errors (ME = 0.53–0.67°C) suggest inadequate adaptability to local microclimates. Additionally, relative humidity products systematically underestimate values in oasis irrigation zones (ME = -0.83% to -1.24%), likely due to insufficient evapotranspiration coupling. ⑷ Spatially, temperature errors are minimal in the Junggar Basin, relative humidity errors dominate southwestern mountainous and oasis areas, and wind speed errors escalate in urban and complex terrain regions. In conclusion, CLDAS products effectively support macro-scale meteorological analysis in the Wuchang region; however, localized microclimate adaptability in complex terrains (e.g., Tianshan Mountains, oasis-desert transition zones) requires further optimization of parameterization schemes and multi-source data integration.