Understanding and forecasting precipitation events in the Arctic maritime environments, such as Bear Island and Ny-{\AA}lesund, is crucial for assessing climate risk and developing early warning systems in vulnerable marine regions. This study proposes a probabilistic machine learning framework for modeling and predicting the dynamics and severity of precipitation. We begin by analyzing the scale-dependent relationships between precipitation and key atmospheric drivers (e.g., temperature, relative humidity, cloud cover, and air pressure) using wavelet coherence, which captures localized dependencies across time and frequency domains. To assess joint causal influences, we employ Synergistic-Unique-Redundant Decomposition, which quantifies the impact of interaction effects among each variable on future precipitation dynamics. These insights inform the development of data-driven forecasting models that incorporate both historical precipitation and causal climate drivers. To account for uncertainty, we employ the conformal prediction method, which enables the generation of calibrated non-parametric prediction intervals. Our results underscore the importance of utilizing a comprehensive framework that combines causal analysis with probabilistic forecasting to enhance the reliability and interpretability of precipitation predictions in Arctic marine environments.

翻译：理解和预测北极海洋环境（如熊岛和尼-奥勒松）的降水事件，对于评估气候风险及在脆弱海区建立早期预警系统至关重要。本研究提出了一种概率机器学习框架，用于建模和预测降水的动态与强度。我们首先利用小波相干分析，探究降水与关键大气驱动因素（如温度、相对湿度、云量和气压）之间的尺度依赖关系，该方法能够捕捉时域和频域上的局部依赖性。为评估联合因果影响，我们采用协同-独特-冗余分解法，量化各变量间交互效应对未来降水动态的影响。这些洞见为开发数据驱动的预报模型提供了依据，该模型同时结合了历史降水数据和因果气候驱动因素。为处理不确定性，我们采用共形预测方法，能够生成校准的非参数预测区间。我们的结果强调了将因果分析与概率预报相结合的综合框架的重要性，以提升北极海洋环境中降水预测的可靠性和可解释性。