Near-term, iterative ecological forecasts can be used to help understand and proactively manage ecosystems. To date, more forecasts have been developed for aquatic ecosystems than other ecosystems worldwide, likely motivated by the pressing need to conserve these essential and threatened ecosystems and increasing availability of high-frequency data. Forecasters have implemented many different modelling approaches to forecast freshwater variables, which have demonstrated promise at individual sites. However, a comprehensive analysis of the performance of varying forecast models across multiple sites is needed to understand broader controls on forecast performance. Forecasting challenges (i.e., community-scale efforts to generate forecasts while also developing shared software, training materials, and best practices) present a useful platform for bridging this gap to evaluate how a range of modelling methods perform across axes of space, time, and ecological systems. Here, we analysed forecasts from the aquatics theme of the National Ecological Observatory Network (NEON) Forecasting Challenge hosted by the Ecological Forecasting Initiative. Over 100,000 probabilistic forecasts of water temperature and dissolved oxygen concentration for 1-30 days ahead across seven NEON-monitored lakes were submitted in 2023. We . For water temperature, we found that forecast skill degraded with increases in forecast horizons, process-based models and models that included air temperature as a covariate generally exhibited the highest forecast performance, and that the most skillful forecasts often Overall, the NEON Forecasting Challenge provides an exciting opportunity for a model inter-comparison to learn about the relative strengths of a diverse suite of models and advance our understanding of freshwater ecosystem predictability.