Understanding how precipitation may change in the future is important for guiding climate change adaptation. Climate models are the primary tools for providing information on future precipitation change, though communicating and interpreting results of different model simulations is challenging. Using an online survey, completed by producers and users of climate model information, we compare and evaluate interpretations of different approaches used to summarise and visualise future climate projections. Results reveal large differences in interpretations of precipitation change arising from choices made in summarising and visualising the data. Respondents interpret significantly smaller ranges of future precipitation change when provided with the multi-model ensemble mean or percentile information, which are commonly used to summarise climate model projections, compared to information about the full ensemble. The ensemble mean is found to be particularly misleading, even when used with information to show model agreement in the sign of change. We conclude that these approaches can lead to distorted interpretations which may impact on adaptation policy and decision-making. To help improve the interpretation and use of climate projections in decision-making, regular testing of visualisations and sustained engagement with target audiences is required to determine the most effective and appropriate visualisation approaches.

了解未来降水如何变化对于指导适应气候变化很重要。气候模型是提供有关未来降水变化信息的主要工具，尽管交流和解释不同模型模拟的结果具有挑战性。使用由气候模型信息的生产者和用户完成的在线调查，我们比较和评估对用于总结和可视化未来气候预测的不同方法的解释。结果表明，在汇总和可视化数据时做出的选择导致的降水变化解释存在很大差异。如果提供了多模式总体平均值或百分位数信息，受访者会解释未来降水变化的范围要小得多，与有关整个集合的信息相比，通常用于总结气候模型的预测。即使将整体平均值与变更迹象中显示模型一致性的信息一起使用，也发现整体平均值特别容易令人误解。我们得出的结论是，这些方法可能会导致解释失真，从而可能影响适应政策和决策。为了帮助改善决策中对气候预测的解释和使用，需要定期进行可视化测试并与目标受众保持持续互动，以确定最有效和最合适的可视化方法。我们得出的结论是，这些方法可能会导致解释失真，从而可能影响适应政策和决策。为了帮助改善决策中对气候预测的解释和使用，需要定期进行可视化测试并与目标受众保持持续互动，以确定最有效和最合适的可视化方法。我们得出的结论是，这些方法可能会导致解释失真，从而可能影响适应政策和决策。为了帮助改善决策中对气候预测的解释和使用，需要定期进行可视化测试并与目标受众保持持续互动，以确定最有效和最合适的可视化方法。