Heatwaves are among the deadliest natural hazards, yet their evolving atmospheric drivers remain unclear. We apply an unsupervised machine learning model to reanalysis data to identify multivariate heatwave patterns over Europe. The model captures seasonally distinct regimes and reveals significant shifts between historical and recent events across all seasons, highlighting changes in heatwave dynamics over time.

The El Niño-Southern Oscillation (ENSO) drives short-term global plant changes, but its influence on decadal changes is not fully known. Using global climate models, we found that plants show pronounced ENSO-driven changes on decadal and longer timescales. Slow plant responses are influenced by soil water content and plant dynamics but only have a weak effect on the plant’s net carbon uptake. Our work shows that vegetation memory could be an important source of decadal climate predictability.