Statistical and Machine Learning Methods for Climate Sciences: Advances, Applications and Emerging Challenges

Dear Colleagues,

Recent advances in statistical and machine learning (ML) methods have revolutionized how climate and hydrometeorological phenomena are investigated. This Special Issue aims to gather studies presenting innovative approaches, practical applications, and methodological developments focused on understanding, predicting, and mitigating the impacts of climate change. Submissions employing classical and modern statistical techniques, as well as ML and deep learning approaches, are welcome for applications such as extreme event detection and prediction, spatio-temporal modeling, dynamic and statistical downscaling, bias adjustment, uncertainty analysis, and integration of multiple data sources (observations, satellites, reanalyses, and climate models). This Special Issue also encourages research exploring teleconnections, ocean–atmosphere interactions, climate services, and scientific communication strategies on climate risk, emphasizing reproducible and open-data approaches. The goal is to foster integration between data science and climatology, highlighting methodological contributions that enhance the predictive capacity, reliability, and applicability of climate analyses across different spatial and temporal scales.

Dr. Daniele Tôrres Rodrigues
Dr. Lára de Melo Barbosa Andrade
Dr. Cláudio Moisés Santos e Silva
Guest Editors

Manuscript Submission Information

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