Extreme weather

Neural networks leverage climate model data to predict how extreme rainfall, hail, and winds will shift geographically as climate changes, accounting for terrain effects.

Climate models show fire weather conditions driven by human warming already detectable in 39% of burnable areas, with dangerous extremes emerging at 2–3°C warming in multiple regions.

Theoretical analysis explains why climate foundation models succeed at field prediction but fail at extreme event detection through in-context learning complexity.

Examine climate change impacts on European brain health through risk management frameworks, addressing tropicalization threats, adaptive capacity, and evidence-based prevention strategies.

Study identifies atmospheric circulation patterns and cross-regional evaporation precursors driving extreme precipitation trends in North China using information flow analysis.

Seven-decade reanalysis reveals sharp increases and spatial shifts in Arctic bioclimatic extremes—droughts, winter warming and rain-on-snow—signaling novel stressors for cold ecosystems.