Rain precipitation prediction is a challenging task as it depends on weather and meteorological features which vary from location to location. As a result, a prediction model that performs well at one location does not perform well at other locations due to the distribution shifts. In addition, due to global warming, the weather patterns are changing very rapidly year by year which creates the possibility of ineffectiveness of those models even at the same location as time passes. In our work, we have proposed an adaptive deep learning-based framework in order to provide a solution to the aforementioned challenges. Our method can generalize the model for the prediction of precipitation for any location where the methods without adaptation fail. Our method has shown 43.51%, 5.09%, and 38.62% improvement after adaptation using a deep neural network for predicting the precipitation of Paris, Los Angeles, and Tokyo, respectively.

翻译：降雨量预测是一项具有挑战性的任务，因其依赖于随地理位置变化的天气和气象特征。因此，由于分布偏移，在某一位置表现良好的预测模型在其他位置效果不佳。此外，受全球变暖影响，天气模式逐年快速变化，这可能导致即使在同一位置，这些模型随着时间推移也会失效。在我们的工作中，我们提出了一种基于深度学习的自适应框架，以解决上述挑战。该方法能够将模型泛化到任意位置的降雨量预测，而传统无自适应方法在类似场景下会失效。采用深度神经网络进行自适应后，我们的方法在巴黎、洛杉矶和东京的降雨量预测性能分别提升了43.51%、5.09%和38.62%。