Southern Peru receives over 60% of its annual climatological precipitation during the short period of January–March. This rainy season precipitation exhibits strong inter‐annual and decadal variability, including severe drought events that incur devastating societal impacts and cause agricultural communities and mining facilities to compete for limited water resources. Improving existing seasonal prediction models of summertime precipitation could aid in water resource planning and allocation across this water‐limited region. While various underlying mechanisms modulating inter‐annual variability have been proposed by past studies, operational forecasts continue to be largely based on rudimentary El Niño‐Southern Oscillation (ENSO)‐based indices, such as Niño3.4, justifying further exploration of predictive skill. To bridge the gap between understanding precipitation mechanisms and operational forecasts, we perform systematic studies on the predictability and prediction skill of southern Peru's rainy season precipitation by constructing statistical forecast models using best available weather station and reanalysis data sets. We construct a simple regression model, based on the principal component (PC) tendency of tropical Pacific sea surface temperatures (SST), and a more advanced linear inverse model (LIM), based on the empirical orthogonal functions of tropical Pacific SST and large‐scale atmospheric variables from reanalysis. Our results indicate that both the PC tendency and LIM models consistently outperform the ENSO‐only based regression models in predicting precipitation at both the regional scale and for individual station, with improvements for individual stations ranging from 10 to over 200%. These encouraging results are likely to foster further development of operational precipitation forecasts for southern Peru.

Agricultural operations in southern Peru are particularly vulnerable to climate variability due to water resource scarcity. In general, the response to drier than normal conditions in this region is reactive and fairly limited due to challenges associated with climate forecasting and administrative capacity to distribute resources. To shift this paradigm, we investigate the potential for an El Niño-Southern Oscillation index-based insurance product. The article presents a demonstration of methodology and application for one specific crop in a department of southern Peru. The purpose of this product is to streamline the ability of decision makers to provide financial relief to affected farmers during, and perhaps before, drought; extending the lead-time of the index that is used to trigger product payouts produces results of similar skill to a product trained on concurrent conditions. Issues explored in this work include basis risk, initial endowment requirements, product longevity, and the potential crossover from index-based insurance to forecast-based financing. The ability of such products to mitigate losses during and after drought may be advantageous in Peru and other regions with notable interannual climate variability.

Abstract. Located at a complex topographic, climatic, and hydrologic crossroads, southern Peru is a semiarid region that exhibits high spatiotemporal variability in precipitation. The economic viability of the region hinges on this water, yet southern Peru is prone to water scarcity caused by seasonal meteorological drought. Meteorological droughts in this region are often triggered during El Niño episodes; however, other large-scale climate mechanisms also play a noteworthy role in controlling the region's hydrologic cycle. An extensive season-ahead precipitation prediction model is developed to help bolster the existing capacity of stakeholders to plan for and mitigate deleterious impacts of drought. In addition to existing climate indices, large-scale climatic variables, such as sea surface temperature, are investigated to identify potential drought predictors. A principal component regression framework is applied to 11 potential predictors to produce an ensemble forecast of regional January-March precipitation totals. Model hindcasts of 51 years, compared to climatology and another model conditioned solely on an El Niño-Southern Oscillation index, achieve notable skill and perform better for several metrics, including ranked probability skill score and a hit-miss statistic. The information provided by the developed model and ancillary modeling efforts, such as extending the lead time of and spatially disaggregating precipitation predictions to the local level as well as forecasting the number of wet-dry days per rainy season, may further assist regional stakeholders and policymakers in preparing for drought.

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Abstract. Coastal flood risk is a serious global challenge facing current and future generations. Several disaster risk reduction (DRR) measures have been posited as ways to reduce the deleterious impacts of coastal flooding. On the global scale, however, efforts to model the effects of DRR measures (beyond structural) in the future are limited. In this paper, we use a global-scale flood risk model to estimate the risk of coastal flooding, and to assess and compare the effectiveness and economic performance of various DRR measures, namely: dykes and coastal levees, dry-proofing of urban assets, zoning restrictions in flood-prone areas, and management of foreshore vegetation. To assess the effectiveness of each DRR measure, we determine the extent to which they can limit future flood risk as a percentage of regional GDP to the same value as today (the so-called relative-risk constant objective). To assess their economic performance, we estimate the economic benefits and costs. If no DRR measures are taken in the future, we estimate expected annual damages to exceed $2 trillion USD by 2080, directly affecting an estimated 15 million people. Over 90 % of sub-national regions in the world can achieve their relative-risk constant targets if at least one of the investigated DRR measures is employed. At the global scale, we find the effectiveness of dykes and coastal levees in achieving the relative-risk constant objective to be 98 %, dry-proofing to be 49 %, zoning restrictions to be 11 %, and foreshore vegetation to be 6 %. In terms of direct costs, the overall figure is largest for dry-proofing ($151 billion) and dykes and coastal levees ($86 billion), much more than those of zoning restrictions ($27 million) and foreshore vegetation ($366 million). While zoning restrictions and foreshore vegetation achieve the highest global benefit-cost ratios, they also provide the least benefits overall. We show that there are large regional patterns in both the effectiveness and economic performance of modelled DRR measures. Future research could assess the indirect costs and benefits of these four and other DRR measures as well as their subsequent hybridisation.