Hyper-resolution land surface modeling provides an unprecedented opportunity to simulate locally relevant water and energy cycles. However, the available meteorological forcing data is often insufficient to fulfill the requirement of hyper-resolution modeling. Here, we developed a comprehensive downscaling framework based on topography-adjusted methods and automated machine learning (AutoML). With this framework, a 90 m atmospheric forcing dataset is developed from ERA5 data at a 0.25° resolution, and the Common Land Model (CoLM) is then forced with the developed forcing data over two complex terrain regions (Heihe and Upper Colorado River basins). We systematically evaluated the downscaled forcing and the CoLM outputs against both in-situ observations and gridded data. The ground-based validation results suggested consistent improvements for all downscaled forcing variables. The downscaled forcings, which incorporated detailed topographic features, offered improved magnitude estimates, achieving a comparable level of performance to that of regional reanalysis forcing data. The downscaled forcing driving the CoLM model show comparable or better skills in simulating water and energy fluxes, as verified by in-situ validations. The hyper-resolution simulations offered a detailed and more reasonable description of land surface processes and attained similar spatial patterns and magnitudes with high-resolution land surface data, especially over highly elevated areas. Additionally, this study highlighted the benefits of using mountain radiation theory-based shortwave radiation downscaling models and AutoML-assisted precipitation downscaling models. These findings emphasized the significance of integrating topography-based downscaling methods for hillslope-scale simulations.