Extreme flood events have regional differences in their generating mechanisms due to the complex interaction of different climate and catchment processes. This study aims to examine the capability of climate drivers to capture year-to-year variability in global flood extremes. Here, we use a statistical attribution approach to model seasonal and annual maximum daily discharge for 7,886 stations worldwide, using seasonand basin-averaged precipitation and temperature as predictors. The results show robust performance of our seasonal climate-informed models in describing the inter-annual variability in seasonal and annual maximum discharges regardless of the geographical region, climate type, basin size, degree of regulation, and impervious area. The developed models enable the assessment of the sensitivity of flood discharge to precipitation and temperature changes, indicating their potential to reliably project changes in the magnitude of flood extremes.Plain Language Summary Successfully modeling the changes in magnitudes of flood events is challenging due to the complexity of flood-driving processes, which vary depending on region and season. Here, we examine how seasonal precipitation and temperature can describe changes in the flood magnitudes. Although we only consider these basic climate factors as predictors, our climate-informed models consistently show a good performance in capturing the year-to-year variability in the magnitude of seasonal and annual flooding events across 7,886 stations worldwide. These results show the suitability of seasonal precipitation and temperature as proxies for different climate and catchment processes leading to flooding.