In June 2018, the Peruvian provinces of Arequipa and Puno in the southern Andean region were affected by heavy snowfall, which caused severe damage to people and livelihoods in several communities. Using the Forecast-based Financing approach, the Peruvian Red Cross implemented its pre-defined early action protocol before this event, after receiving an extreme snowfall warning (Level 4) from the Peruvian meteorological service. Here, we provide a case study of the approach and event itself, documenting the decision-making thresholds as well as the actions taken. This warning activated the thresholds established in the protocol, and Peruvian Red Cross prioritized 10 communities for pre-disaster support based on the forecasted severity of the event in combination with vulnerability and exposure information. The activation took place 2 days before the extreme snowfall in the communities, and...

Weather forecasts, climate change projections, and epidemiological predictions all represent domains that are using forecast data to take early action for risk management. However, the methods and applications of the modeling efforts in each of these three fields have been developed and applied with little cross-fertilization. This perspective identifies best practices in each domain that can be adopted by the others, which can be used to inform each field separately as well as to facilitate a more effective combined use for the management of compound and evolving risks. In light of increased attention to predictive modeling during theCOVID-19 pandemic,we identify three major areas that all three of these modeling fields should prioritize for future investmentand improvement: (1) decision support, (2) conveying uncertainty, and (3) capturingvulnerability.