Analyzing and utilizing spatiotemporal big data are essential for studies concerning climate change. However, such data are not fully integrated into climate models owing to limitations in statistical frameworks. Herein, we employ VARENN (visually augmented representation of environment for neural networks) to efficiently summarize monthly observations of climate data for 1901–2016 into two-dimensional graphical images. Using red, green, and blue channels of color images, three different variables are simultaneously represented in a single image. For global datasets, models were trained via convolutional neural networks. These models successfully classified the rises and falls in temperature and precipitation. Moreover, similarities between the input and target variables were observed to have a significant effect on model accuracy. The input variables had both seasonal and interannual variations, whose importance was quantified for model efficacy. We successfully illustrated the importance of short-term (monthly) fluctuations in the model accuracy, suggesting that our AI-based approach grasped some previously unknown patterns that are indicators of succeeding climate trends. VARENN is thus an effective method to summarize spatiotemporal data objectively and accurately.

分析和利用时空大数据对于有关气候变化的研究至关重要。但是，由于统计框架的局限性，此类数据尚未完全纳入气候模型。在本文中，我们采用VARENN（神经网络环境的可视化增强表示）将1901-2016年的每月气候数据观测有效地汇总为二维图形图像。使用彩色图像的红色，绿色和蓝色通道，可以在单个图像中同时表示三个不同的变量。对于全局数据集，通过卷积神经网络训练模型。这些模型成功地对温度和降水的上升和下降进行了分类。此外，观察到输入变量和目标变量之间的相似性对模型准确性有重大影响。输入变量具有季节性和年度间变化，其重要性已通过模型有效性进行了量化。我们成功地说明了模型准确性短期（每月）波动的重要性，表明我们基于AI的方法掌握了一些以前未知的模式，这些模式是成功的气候趋势的指标。因此，VARENN是客观准确地汇总时空数据的有效方法。