Heavy rainfall extremes are intensifying with warming at a rate generally consistent with the increase in atmospheric moisture, for accumulation periods from hours to days.• In some regions, high-resolution modeling, observed trends and observed temperature dependencies indicate stronger increases in short-duration, sub-daily, extreme rainfall intensities, up to twice what would be expected from atmospheric moisture increases alone.• Stronger local increases in short-duration extreme rainfall intensities are related to convective cloud feedbacks but their relevance to climate change is uncertain due to modulation by changes to temperature stratification and large-scale atmospheric circulation• The evidence is unclear whether storm size will increase or decrease with warming; however, increases in rainfall intensity and the spatial footprint of the storm can compound to give significant increases in the total rainfall during an event.• Evidence is emerging that sub-daily rainfall intensification is related to an intensification of flash flooding, at least locally. This will have serious implications for flash flooding on much of the planet and requires urgent climate-change adaptation measures.

Compound events (CEs) are weather and climate events that result from multiple hazards or drivers with the potential to cause severe socio-economic impacts. Compared with isolated hazards, the multiple hazards/drivers associated with CEs can lead to higher economic losses and death tolls. Here, we provide the first analysis of multiple multivariate CEs potentially causing high-impact floods, droughts, and fires. Using observations and reanalysis data during 1980–2014, we analyse 27 hazard pairs and provide the first spatial estimates of their occurrences on the global scale. We identify hotspots of multivariate CEs including many socio-economically important regions such as North America, Russia and western Europe. We analyse the relative importance of different multivariate CEs in six continental regions to highlight CEs posing the highest risk. Our results provide initial guidance to assess the regional risk of CE events and an observationally-based dataset to aid evaluation of climate models for simulating multivariate CEs.

Anthropogenic climate change is expected to affect global river flow. Here, we analyze time series of low, mean, and high river flows from 7250 observatories around the world covering the years 1971 to 2010. We identify spatially complex trend patterns, where some regions are drying and others are wetting consistently across low, mean, and high flows. Trends computed from state-of-the-art model simulations are consistent with the observations only if radiative forcing that accounts for anthropogenic climate change is considered. Simulated effects of water and land management do not suffice to reproduce the observed trend pattern. Thus, the analysis provides clear evidence for the role of externally forced climate change as a causal driver of recent trends in mean and extreme river flow at the global scale.