近年来，中国西南地区，包括云贵川渝等地，频繁受到气候变化引发的洪涝灾害，造成了严重的人员伤亡和巨大的财产损失，这些灾害的频发与降水异常密切相关。尽管传统的统计方法和大气模式在降水预测方面已有一定成效，但面对降水数据复杂的时空特征，仍缺乏有效的处理手段。随着机器学习技术的发展，融合了卷积神经网络（Convolutional Neural Network，CNN）和长短期记忆网络（LongShort-Term Memory，LSTM）的卷积长短期记忆网络（Convolutional Long Short-Term Memory，ConvL‐STM）在研究时空序列问题方面表现较为突出，特别是在降水预测领域。为了更准确地预测中国西南地区未来一年的夏季降水（降水短期气候预测），本文构建了一个融合全球海表温度和中国西南地区降水数据的数据集，采用ConvLSTM进行训练，并将其命名为SST-ConvLSTM。该模型不仅能够捕捉实况降水数据中的时空特征，而且能够从全球海表温度数据中学习到一些信息，从而增强降水短期气候预测的准确性。研究结果显示：相较于不考虑海表温度的ConvLSTM和大气模式，SST-ConvLSTM模型在中国西南地区夏季降水短期气候预测方面具有显著优势。（1）在数值方面，SST-ConvLSTM模型的预测结果与实况降水数据最为接近，且变化趋势相似。相比之下，ConvLSTM和传统大气模式的预测结果均存在一定程度的偏差。（2）在空间分布上，SST-ConvLSTM模型同样表现较好。其预测结果与实况降水数据的分布较为一致，能够准确反映降水在空间上的分布。（3）在模型评估中，本文采用了三种评估指标对 SSTConvLSTM模型的性能进行了评价。结果显示，SST-ConvLSTM模型在各项评估指标上均表现较好，取得了最佳成绩。这些发现为未来西南地区的降水预测研究提供了重要的参考和启示。

In recent years，Southwestern China，including Yunnan，Guizhou，Sichuan，and Chongqing，has been frequently hit by flood disasters caused by climate change，resulting in severe casualties and enormous property losses. The occurrence of these disasters is closely related to abnormal precipitation. Although traditional statistical methods and atmospheric models have achieved certain effectiveness in precipitation forecasting，effective approaches for dealing with the complex spatiotemporal characteristics of precipitation data are still lacking. With the development of machine learning technology，the convolutional long short-term memory network（Con‐ vLSTM），which integrates convolutional neural networks （CNN） and long short-term memory networks （LSTM），has shown outstanding performance in addressing spatiotemporal sequence problems，particularly in the field of precipitation forecasting. In order to more accurately predict the summer precipitation in the south‐ western region of China for the next year（short-term climate prediction of precipitation），this study constructed a dataset by integrating global sea surface temperature and precipitation data in Southwestern China. The ConvL‐ STM was used for training and named SST-ConvLSTM. This model not only captures the spatiotemporal characteristics in real precipitation data but also learns some information from global sea surface temperature data， thereby enhancing the accuracy of short-term climate prediction of precipitation. The results show that compared to ConvLSTM that does not consider sea surface temperature and a traditional atmospheric model，SST-ConvL‐ STM model has significant advantages in short-term climate prediction of summer precipitation in Southwestern China.（1）Numerically，the predictions of the SST-ConvLSTM model are closest to the real precipitation data， with similar trend changes. In contrast，both ConvLSTM and the traditional atmospheric model show certain de‐ viations in their predictions.（2）Spatially，the SST-ConvLSTM model also performs well. Its predictions are consistent with the spatial distribution of real precipitation data and accurately reflect the spatial distribution of precipitation.（3）In model evaluation，three evaluation metrics were used to assess the performance of the SSTConvLSTM model. The results show that the SST-ConvLSTM model performs well in all evaluation metrics and achieves the best scores. These findings provide important references and insights for future research on precipitation prediction in Southwestern China.