Recently, deep-learning weather forecasting models have surpassed traditional numerical models in terms of the accuracy of meteorological variables. However, there is considerable potential for improvements in precipitation forecasts, especially for heavy precipitation events. To address this deficiency, we propose Leadsee-Precip, a global deep learning model to generate precipitation from meteorological circulation fields. The model utilizes an information balance scheme to tackle the challenges of predicting heavy precipitation caused by the long-tail distribution of precipitation data. Additionally, more accurate satellite and radar-based precipitation retrievals are used as training targets. Compared to artificial intelligence global weather models, the heavy precipitation from Leadsee-Precip is more consistent with observations and shows competitive performance against global numerical weather prediction models. Leadsee-Precip can be integrated with any global circulation model to generate precipitation forecasts. But the deviations between the predicted and the ground-truth circulation fields may lead to a weakened precipitation forecast, which could potentially be mitigated by further fine-tuning based on the predicted circulation fields.

翻译：近年来，深度学习天气预报模型在气象变量的预报精度上已超越传统数值模型。然而，在降水预报，尤其是强降水事件的预报方面，仍有相当大的改进潜力。为弥补这一不足，我们提出了Leadsee-Precip，一个从气象环流场生成降水的全球深度学习模型。该模型采用一种信息平衡方案，以应对因降水数据的长尾分布所带来的强降水预测挑战。此外，模型使用更精确的基于卫星和雷达的降水反演数据作为训练目标。与人工智能全球天气模型相比，Leadsee-Precip的强降水预报结果与观测数据更为一致，并在与全球数值天气预报模型的对比中展现出有竞争力的性能。Leadsee-Precip可与任何全球环流模型集成以生成降水预报。但预测环流场与真实环流场之间的偏差可能导致降水预报效果减弱，这一问题或可通过基于预测环流场的进一步微调来缓解。