Global climate signals of floods in near-natural rivers

Floods are hydro-climatic extremes with severe socioeconomic and environmental consequences. Many studies have examined how large-scale modes of climate variability (e.g., ENSO, NAO) influence floods, but many have relied on catchments influenced by anthropogenic activities, which obscure underlying climate-flood relationships. Here, we use the newly released ROBIN Reference Hydrometric Network, a global dataset of over 3,000 near-natural catchments with daily streamflow records, to provide an observational assessment of climate-flood relationships at the global scale. We first quantify long-term and multi-temporal trends in annual flood peaks and peak-over-threshold events and evaluate their connections with key modes of climate variability across different IPCC regions. Trend analysis reveals how flood metrics have evolved across regions and time periods, while correlation analysis reveals the modes of climate variability that are associated with year-to-year variations in flood peaks and frequencies. A signal-to-noise framework tests whether global mean surface temperature leaves a detectable fingerprint on high flow regimes. This analysis helps to clarify the extent to which climate variability influences flood occurrence and magnitude in near-natural catchments worldwide. Moreover, we propose a machine learning-based process attribution framework to identify climate and catchment controls on floods in near-natural catchments. Preliminary results indicate substantial spatial variability in dominant flood drivers across and within IPCC regions and suggest that large-scale atmospheric circulation modes exert strong, but regionally distinct, influence on seasonal flood frequency. Overall, our findings underscore the importance of regional climate modes in modulating floods and provide the first global baseline on climate-driven changes to floods in near-natural catchments.