Climate models struggle to accurately simulate precipitation, particularly extremes and the diurnal cycle. Here, we present a hybrid model that is trained directly on satellite-based precipitation observations. Our model runs at 2.8$^\circ$ resolution and is built on the differentiable NeuralGCM framework. The model demonstrates significant improvements over existing general circulation models, the ERA5 reanalysis, and a global cloud-resolving model in simulating precipitation. Our approach yields reduced biases, a more realistic precipitation distribution, improved representation of extremes, and a more accurate diurnal cycle. Furthermore, it outperforms the mid-range precipitation forecast of the ECMWF ensemble. This advance paves the way for more reliable simulations of current climate and demonstrates how training on observations can be used to directly improve GCMs.

翻译：气候模型在准确模拟降水方面存在困难，特别是对极端降水和日循环的模拟。本文提出了一种直接基于卫星降水观测数据进行训练的混合模型。该模型以2.8$^\circ$分辨率运行，构建于可微分NeuralGCM框架之上。在降水模拟方面，该模型相较于现有大气环流模型、ERA5再分析数据以及全球云解析模型均展现出显著改进。我们的方法实现了偏差降低、降水分布更趋真实、极端事件表征能力提升以及日循环模拟精度提高。此外，其表现优于欧洲中期天气预报中心集合预报的中期降水预测。这一进展为更可靠地模拟当前气候开辟了新途径，并展示了如何利用观测数据训练直接改进大气环流模型。