Enhancing ERA5 precipitation with improved predictor selection for regional climate change assessment

Muhammed Zakir Keskin (), Ahmad Abu Arra (), Ercan Gemici () and Eyüp Şişman ()
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Muhammed Zakir Keskin: Bartın University
Ahmad Abu Arra: Yildiz Technical University
Ercan Gemici: Bartın University
Eyüp Şişman: Yildiz Technical University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2025, vol. 121, issue 18, No 40, 21810 pages

Abstract: Abstract Accurate regional precipitation projections are critical for effective climate impact assessment and adaptation planning. This study presents a novel methodology for enhancing ERA5 reanalysis precipitation data through optimized predictor selection and statistical downscaling using the Multivariate Adaptive Regression Splines (MARS) algorithm. Four distinct predictor selection scenarios: a full 26-variable model, a reduced 14-variable model based on correlation and physical relevance, a compact 6-variable model emphasizing simplicity, and a station-specific model derived from All Possible Regression (APR), were used along with the MARS algorithm. Predictor variables were selected through traditional correlation analyses (Pearson and Spearman), the APR-based approach, and performance-based evaluation using MARS. The resulting downscaled models were evaluated using different performance metrics, including Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE), normalized Root Mean Square Error (nRMSE), and the coefficient of determination (R²). The Western Black Sea Basin in Türkiye, with monthly precipitation data from 32 meteorological stations (1979–2023), was selected as an application to apply the newly proposed dual-stage approach. Results demonstrated that all MARS-enhanced models significantly outperformed the raw ERA5 data, particularly in inland regions where ERA5 performance was initially poor. The APR-based model emerged as the top performer across most stations, while the 6-variable model provided a strong balance between accuracy and simplicity. While the nRMSE initially reached around 77% at some stations, it was significantly reduced to 24.6%, 29%, 26.4%, and 25.1% under the 26-variable, 14-variable, 6-variable, and APR scenarios. The KGE nearly doubled, reaching approximately 0.7–0.9 across all scenarios, confirming the substantial improvement applied to the ERA5 precipitation data. This approach, integrating correlation-based and predictive performance-driven variable selection, proved effective in refining regional precipitation projections. The methodology can be adapted to other regions or climate variables, offering a replicable framework for improving the usability of reanalysis data in hydrological and climate impact studies.

Keywords: Predictor selection; Reanalysis data; MARS algorithm; Regional statistical downscaling; ERA5 precipitation enhancement; Türkiye (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11069-025-07664-8

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