Although global circulation models (GCMs) have been used for the reconstruction of precipitation for selected geological time slices, there is a lack of a coherent set of precipitation models for the Mesozoic-Cenozoic period (the last 250 million years). There has been dramatic climate change during this time period capturing a super-continent hothouse climate, and continental breakup and dispersal associated with successive greenhouse and ice-house climate periods. We present an approach that links climate-sensitive sedimentary deposits such as coal, evaporites and glacial deposits to a global plate model, reconstructed paleo-elevation maps and high-resolution GCMs via Bayesian machine learning. We model the joint distribution of climate-sensitive sediments and annual precipitation through geological time, and use the dependency between sediments and precipitation to improve the models predictive accuracy. Our approach provides a set of 13 data-driven global paleo-precipitation maps between 14 and 249 Ma, capturing major changes in long-term annual rainfall patterns as a function of plate tectonics, paleo-elevation and climate change at a low computational cost.

尽管已使用全球环流模型（GCM）重建了特定地质时间片的降水量，但在中生代-新生代（最近的2.5亿年）中缺乏一套连贯的降水量模型。在这段时间里，发生了戏剧性的气候变化，捕捉到了超级大陆的温室气候，以及随后的温室和冰屋气候时期引起的大陆破裂和扩散。我们提出一种方法，通过贝叶斯机器学习将对气候敏感的沉积物（例如煤，蒸发物和冰川沉积物）与全球板块模型，重建的古高程图和高分辨率GCM关联起来。我们通过地质时间来模拟气候敏感沉积物和年降水量的联合分布，并利用沉积物和降水之间的依存关系来提高模型的预测准确性。我们的方法提供了一组13个数据驱动的14-249 Ma之间的全球古降水图，以较低的计算成本捕获了长期年降水量模式的主要变化，这些变化是板块构造，古海拔和气候变化的函数。