Predicting shifts in local extreme weather under global warming is key for climate adaptation, but climate projections lack detail. A new tool, EXSoDOS (DOwnScaling of weather EXtremes Shifts), combines ground measurements, reanalysis data, and climate models to improve estimates of extreme weather, aiding better risk planning. Tested in five regions, it accurately captures temperature, rainfall, and wind extremes including their past changes, outperforming raw model data. Results show worsening heat (stress) and precipitation by 2100.

Boreal peatlands store much of the global soil carbon and the service is closely related to nutrient cycling. This study improved a major land surface model to better represent how plants gain nitrogen and phosphorus through fine roots and mycorrhizal association. The new model more accurately captured observed carbon fluxes than the default model at an experimental site in Minnesota, and suggests shifts in nutrient uptake strategy mitigates transition from carbon sink to source under warming.