Uni- and multivariate bias adjustment methods in Nordic catchments: Complexity and performance in a changing climate

Area of Science:

• Hydrology
• Climatology
• Environmental Science

Background:

• Climate model outputs require downscaling and bias adjustment for catchment-scale impact studies.
• Various statistical methods, from univariate to multivariate, exist for bias adjustment.
• Guidance on selecting appropriate bias adjustment methods, considering complexity and benefits, is lacking.

Purpose of the Study:

• To comprehensively evaluate two univariate and two multivariate bias adjustment methods.
• To assess method performance in reproducing precipitation and temperature series features.
• To provide insights into method selection for climate-change impact studies in high latitudes.

Main Methods:

• Evaluation of two common univariate and two multivariate bias adjustment techniques.
• Assessment of performance on univariate, multivariate, and temporal features of climate data.
• Analysis of trade-offs between method benefits (skill, added value) and disadvantages (complexity, computational demand).

Main Results:

• All evaluated bias adjustment methods generally improved raw climate model simulations.
• No single bias adjustment method consistently outperformed all others across all features.
• Performance differences were observed for specific statistical features, highlighting method-specific strengths.

Conclusions:

• Bias adjustment methods enhance climate model simulations for hydrological studies.
• Method selection should balance statistical performance with practical considerations like computational resources and theoretical demands.
• The choice of bias adjustment method impacts the reliability and applicability of climate-change impact assessments.