The HL-LHC program has seen numerous extrapolations of its needed computing resources that each indicate the need for substantial changes if the desired HL-LHC physics program is to be supported within the current level of computing resource budgets. Drivers include detector upgrades, large increases in event complexity (leading to increased processing time and analysis data size) and trigger rates needed (5-10 fold increases) for the HL-LHC program. In this presentation, we discuss the newly developed modeling techniques in use for improving the accuracy of CMS computing resource needs for HL-LHC. Our emphasis is on monitoring-data driven techniques for model construction, parameter determination, and importantly, model extrapolations. Additionally we focus on uncertainty quantification as a critical component for understanding and properly interpreting our results.