Subseasonal-to-seasonal (S2S) prediction skill is of great importance for both scientific research and practical applications, such as agriculture, water management, and disaster preparedness. This study compares the predictive skill of two climate models: E3SMv2.1 and CESM2. We generated a collection of 11-member, 45-day hindcasts for each model, initialized every Monday from 1999 to 2020. Our analysis focuses on the skill of predicting global surface temperature and precipitation, providing a comprehensive evaluation of each model's capabilities. Additionally, we calculate MJO skill and conduct case studies to illustrate the models' performance during heat waves. Finally, we combine both datasets in an effort to reduce model bias and error. The datasets from these hindcasts will create countless opportunities for further research into the predictability of subseasonal weather and its most critical drivers. This comparative study aims to provide insights that enhance prediction efforts by the Department of Energy and the National Science Foundation, contributing to the advancement of S2S forecasting. Ultimately, we demonstrate the performance of these two model frameworks on S2S timescales and provide explanations for their differences.