Bracing for impact: how shifting precipitation extremes may influence physical climate risks in an uncertain future.

As extreme precipitation intensifies under climate change, traditional risk models based on the '100-year return period' concept are becoming inadequate in assessing real-world risks. In response, this nationwide study explores shifting extremes under non-stationary warming using high-resolution data across the contiguous United States. Results reveal pronounced variability in 100-year return levels, with Coastal and Southern regions displaying the highest baseline projections, and future spikes are anticipated in the Northeast, Ohio Valley, Northwest, and California.

Remote sensing-enabled machine learning for river water quality modeling under multidimensional uncertainty.

Two fundamental problems have inhibited progress in the simulation of river water quality under climate (and other) uncertainty: 1) insufficient data, and 2) the inability of existing models to account for the complexity of factors (e.g., hydro-climatic, basin characteristics, land use features) affecting river water quality.