General circulation model (GCM) biases are one of the important sources of biases and uncertainty in dynamic downscaling–based simulations. The ability of regional climate models to simulate tropical cyclones (TCs) is strongly affected by the ability of GCMs to simulate the large-scale environmental field. Thus, in this work, we employ a recently developed multivariable integrated evaluation method to assess the performance of 33 CMIP6 (phase 6 of the Coupled Model Intercomparison Project) models in simulating multiple fields. The CMIP6 models are quantitatively evaluated against two reanalysis datasets over five ocean areas. The results show that most of the CMIP6 models overestimate the mid-level humidity in almost all tropical oceans. The multi-model ensemble mean overestimates the vertical shear of the horizontal winds in the Northeast Pacific and North Atlantic. An increase in model horizontal resolution appears to be helpful in improving the model simulations. For example, there are 6–8 models with higher resolution among the top 10 models in terms of overall model performance in simulating the climatology and interannual variability of multiple variables. Similarly, there are 7–8 models with lower resolution among the bottom 10 patterns. The model skill varies depending on the region and variable being evaluated. Although no model performs best in all regions and for all variables, some models do show relatively good capability in simulating the large-scale environmental field of TCs. For example, the MPI-ESM1-2-LR, MPI-ESM1-2-HR, and FIO-ESM-2-0 models show relatively good skill in simulating the climatology and interannual variability of the large-scale environmental field in the Northern and Southern Hemispheres.