Toward Cleaner Extremes: A GEV-Based Framework for Identifying and Removing Implausible Annual Maxima in Climate Projections
Abstract
Accurate identification of outliers in precipitation and geoscience datasets is critical for ensuring the reliability of projections and subsequent analyses. Traditional methods, such as statistical techniques and observational comparisons, often fall short in addressing the complexities of large-scale climate data. In this study, we propose a novel methodology that integrates statistical techniques with a probabilistic framework to systematically detect and remove implausible extreme values while preserving the integrity of the dataset. Our method combines the generalized extreme value (GEV) distribution, L-moment fitting, and extensive Monte Carlo simulations to establish robust thresholds for outlier detection. By selecting a probabilistic threshold tied to exceptionally rare events, our approach effectively isolates highly improbable values while minimizing false positives and preserving statistically plausible extremes. The proposed methodology offers a scalable and reliable solution for managing vast and complex datasets, with significant implications for climate modeling and geoscience research.
