Effective selection of climate predictors is a fundamental aspect of climate modeling research. Predictor Screening (PS) plays a crucial role in identifying regional climate drivers, reducing noise, expediting convergence, and minimizing time consumption, ultimately leading to the development of robust models. This review delves into the complex landscape of PS techniques within the context of Numerical Climate Modeling (NCM), with a specific focus on their applicability across various Köppen climate classifications and PS model structures. The analysis revealed substantial variations in the performance of PS methods, shedding light on their ability to capture –and prioritize predictors related to precipitation and temperature within distinct climate contexts. Furthermore, the provided methods have been categorized into two subsections: Feature Selection (FS) and Feature Extraction (FE), with FS encompassing filter, wrapper, embedded, and ensemble/hybrid techniques, and FE covering Linear Feature Extraction (LFE), Time-Domain Analysis (TDA), deep learning, and clustering methods. The initial compilation of papers, acquired through a keyword search on Scopus, consisted of 3650 documents. Following a meticulous evaluation process, 206 papers were identified as fitting for inclusion in the literature review, covering the time frame from 1974 to November 3, 2023. In conclusion, the results provide a detailed understanding of the strengths and limitations of each approach, establishing a hierarchy of effectiveness contingent upon the specific climate context. Additionally, insights into promising avenues for future research in this field are offered. This review follows the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) standard as its foundation.

中文翻译：

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数值气候模型降尺度预测筛选方法的系统回顾


气候预测因子的有效选择是气候模拟研究的一个基本方面。预测筛选（PS）在识别区域气候驱动因素、减少噪音、加速收敛和最大限度地减少时间消耗方面发挥着至关重要的作用，最终导致稳健模型的开发。本综述深入探讨了数值气候模型 (NCM) 背景下 PS 技术的复杂情况，特别关注其在各种柯本气候分类和 PS 模型结构中的适用性。分析揭示了 PS 方法性能的巨大差异，揭示了它们在不同气候背景下捕获和优先考虑与降水和温度相关的预测因子的能力。此外，所提供的方法分为两小节：特征选择（FS）和特征提取（FE），其中 FS 涵盖过滤器、包装器、嵌入式和集成/混合技术，FE 涵盖线性特征提取（LFE）、时间-领域分析（TDA）、深度学习和聚类方法。通过 Scopus 上的关键词搜索获得的论文最初汇编包含 3650 篇文档。经过细致的评估过程，206 篇论文被确定为适合纳入文献综述，涵盖的时间范围为 1974 年至 2023 年 11 月 3 日。总而言之，结果提供了对每种方法的优点和局限性的详细了解，建立了取决于具体气候背景的有效性等级。此外，还提供了对该领域未来研究有希望的途径的见解。本次审查遵循 PRISMA（系统审查和荟萃分析的首选报告项目）标准作为基础。