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Abstract Summer rainfall in the southeast Prairie Pothole Region (SEPPR) is an important part of a vital wetland ecosystem that various species use as their habitat. We examine sources and pathways for summer rainfall moisture, large‐scale features influencing moisture delivery, and large‐scale connections related to summer moisture using the Hybrid Single‐Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Analysis of HYSPLIT back trajectories shows that land is the primary moisture source for summer rainfall events indicating moisture recycling plays an important role in precipitation generation. The Great Plains Low‐Level Jet/Maya Express is the most prominent moisture pathway. It impacts events sourced by land and the Gulf of Mexico (GoM), the secondary moisture source. There is a coupling between land, atmosphere, and ocean conveyed by large‐scale climate connections between rainfall events and sea surface temperature (SST), Palmer Drought Severity Index, and 850‐mb heights. Land‐sourced events have a connection to the northern Pacific and northwest Atlantic Oceans, soil moisture over the central U.S., and low‐pressure systems over the SEPPR. GoM‐sourced events share the connection to soil moisture over the central U.S. but also show connections to SSTs in the North Pacific and Atlantic Oceans and the GoM, soil moisture in northern Mexico, and 850‐mb heights in the eastern Pacific Ocean. Both types of events show connections to high 850‐mb heights in the Caribbean which may reflect a connection to Bermuda High. These insights into moisture sources and pathways can improve skill in SEPPR summer rainfall predictions and benefit natural resource managers in the region.

The Prairie Pothole Region (PPR), located in central North America, is an important region hydrologically and ecologically. Millions of wetlands, many containing ponds, are located here, and they serve as habitats for various biota and breeding grounds for waterfowl. They also provide carbon sequestration, sediment and nutrient attenuation, and floodwater storage. Land modification and climate change are threatening the PPR, and water and wildlife managers face important conservation decisions due to these threats. We developed predictive, multisite forecasting models using canonical correlation analysis (CCA) for pond counts in the southeast PPR, the portion located within the United States, to aid in these important decisions. These forecast models predict spring (May) and summer (July) pond counts for each region (stratum) of the United States Fish and Wildlife Service’s pond and waterfowl surveys using a suite of antecedent, large-scale climate variables and indices including 500 millibar heights, sea surface temperatures (SSTs), and Palmer Drought Severity Index (PDSI). Models were developed to issue forecasts at the start of all preceding months beginning on March 1st. The models were evaluated for their performance in a predictive mode by leave-one-out cross-validation. The models exhibited good performance ( R values above 0.6 for May forecasts and 0.4 for July forecasts), with performance increasing as lead time decreased. This simple and versatile modeling approach offers a robust tool for efficient management and sustainability of ecology and natural resources. It demonstrates the ability to use large-scale climate variables to predict a local variable in a skilful way and could serve as an example to develop similar models for use in management and conservation decisions in other regions and sectors of the environment.