A machine learning framework built on public grid load data classifies daily stress regimes across PJM and CAISO, revealing that the two largest US power systems fail in fundamentally different ways - California through compound solar-demand events, PJM through sustained heat-dome loading - with implications for how operators anticipate and respond to extreme grid stress.