Understanding impacts of climate extremes is very important for society and the economy. We identify three challenges restricting this understanding: limited availability and quality of impact data, difficulties in understanding why given impacts occur and lack of reliable projections of future impacts. We also identify key opportunities, including newly released datasets, recent methodological and technical advances and interdisciplinary collaborations between the social and natural sciences.

This paper proposes a new framework combining scaling theory with positive tipping points to explain how regenerative agriculture can scale rapidly. Drawing on the TIST programme (The International Small group and Tree planting programme) in East Africa, it shows that enabling conditions – like affordability, accessibility, and social trust – can trigger feedback loops such as social contagion and network effects. However, outcomes remain highly context-specific, requiring tailored approaches for sustained adoption.

AI is impacting science, providing key data insights, but most algorithms are statistical requiring cautious "out-of-sample" extrapolation. Yet climate research concerns predicting future climatic states. We consider a new method of AI-led equation discovery. Equations offer process interpretation and more robust predictions. We recommend this method for climate analysis, suggesting illustrative application to atmospheric convection, land–atmosphere CO₂ flux, and global ocean circulation models.