Climate change models are being used to develop large-scale national and international strategies and policies for a sustainable future. Predictions of global warming and precipitation variability are becoming driving forces in long-range planning and management of agricultural research and production. Although climate models are potentially important decision-making tools for crop research and production, their utility remains limited because the climate models predict general changes across large geographical regions. Currently, climate models are being downscaled to develop higher resolution predictions of climate change at the local scale. Accurate predictions of future local climate conditions could be particularly useful for crop scientists since plant production in any given area depends on local rather than global weather. The amount of climate data is expanding exponentially as advances in satellite and radar technologies enhance the collection and storage of environmental data. In stark contrast, field-based weather data remain sparse despite their importance to climate change research. Collecting in-situ weather data is time-consuming, labor-intensive, and woefully underfunded yet essential for assessing, validating, improving, and interpreting complex and controversial climate models. It is incumbent on crop scientists to venture across disciplines and develop collaborations with climate scientists to create a user-friendly, accessible database as a critical planning tool in a world of uncertainty and change.