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Our broad research goal is to understand how human societies adapt to natural hazards, such as droughts and floods, and how their social and cultural structures are shaped by these events. Here we develop meteorological data of extreme dry, wet, cold, and warm indices relative to 96 largely nonindustrial societies in the worldwide Standard Cross-Cultural Sample to explore how well the meteorological data can be used to hindcast ethnographically reported drought and flood events and the global patterns of extremes. We find that the drought indices that are best at hindcasting ethnographically reported droughts [precipitation minus evaporation (P − E) measures] also tend to overpredict the number of droughts, and therefore we propose a combination of these two indices plus the PDSI as an optimal approach. Some wet precipitation indices (R10S and R20S) are more effective at hindcasting ethnographically reported floods than others. We also calculate the predictability of those extreme indices and use factor analysis to reduce the number of variables so as to discern global patterns. This work highlights the ability to use extreme meteorological indices to fill in gaps in ethnographic records; in the future, this may help us to determine relationships between extreme events and societal response over longer time scales than are otherwise available.