Probable maximum precipitation (PMP) is an engineering standard adopted in the United States for designing high-hazard infrastructure. PMP has been estimated based on observations contained in storm catalogs, with an implicit assumption that storms observed in the past adequately represent the risk of extreme rainfall now and in the future. However, multiple lines of evidence indicate that extreme rainfall increases with warmer temperatures, calling for a reconsideration of the current PMP estimation procedure. To incorporate major scientific advances in understanding and modeling of extreme rainfall as well as to account for climate non-stationarity, the 2024 National Academies report “Modernizing Probable Maximum Precipitation Estimation” recommended a new definition of PMP based on annual exceedance probability and different uses of numerical modeling in a phased approach for modernizing PMP estimation. Foundational to model-based estimation of PMP is kilometer-scale or finer-resolution models necessary to resolve storms that produce PMP-magnitude precipitation. An important component of the phased approach is a Model Evaluation Project (MEP), which will provide scientific grounding for model-based PMP estimation. In this presentation, we will discuss the multiple lines of evidence for the warming effects on extreme rainfall, the proposed phased approach to model-based PMP estimation that fully incorporates the effect of climate change, and the MEP. Besides supporting model-based PMP estimation in the future, the kilometer-scale simulations envisioned in the report will also provide critically needed information for assessing future changes in hazards that are often coupled with extreme rainfall, including coastal storm surge and compound flooding.