Accurate simulation of land surface processes is important for understanding global water and energy cycles, as offline land surface models (LSMs) are heavily dependent on the quality of meteorological forcing data. The Japanese Meteorological Agency has developed JRA-3Q, a new-generation atmospheric reanalysis that succeeds JRA-55. This new dataset provides enhanced spatial resolution, improved representation of physical processes, and advanced data assimilation systems. This study evaluates the performance of JRA-3Q as forcing data for the CoLM2024 LSM, comparing it with JRA-55, with a focus on simulating hydrological processes. We conducted comprehensive model simulations from 2001 to 2010 using the False Discovery Rate (FDR) method to evaluate the statistical significance of variables and Partial Least Squares Regression (PLSR) to quantify attribution mechanisms between forcing variables and simulation outputs. Results show that JRA-3Q significantly improves the representation of precipitation, especially in arid regions, as well as air temperature, with these enhancements propagating through the LSM to improve global hydrological variables (evapotranspiration, surface soil moisture, total runoff, and streamflow). These advancements support drought detection, flood forecasting, and climate impact research. Attribution analysis reveals that hydrological improvements in arid regions benefit from enhanced precipitation accuracy. However, insufficient shortwave radiation in tropical regions limits the simulation of net radiation. Furthermore, increasing the simulation resolution does not necessarily enhance model performance, and the inclusion of dynamic leaf area index and biogeochemical modules introduces additional uncertainties, highlighting the complexity-performance trade-off. These findings emphasize the importance of regional characteristics in forcing data selection and suggest optimizing tropical radiation representation within integrated evaluation frameworks.