Two dimensional bias correction of temperature and precipitation copulas in climate models

Abstract

In common climate model bias‐correction procedures, temperature and precipitation are corrected separately, thereby degrading the dynamical link represented within the model. We propose a methodology that advances the state‐of‐the‐art by correcting not just the 1D intensity distributions separately but the full two‐dimensional statistical distribution. To assess the effectiveness of the proposed method, it is applied to the REMO regional climate model output using point measurements of hourly temperature and precipitation from 6 weather stations over Germany as observations. A standard cross‐validation is performed by dividing the data into two nonoverlapping 15 year periods. Results show that the methodology effectively improves the temperature‐precipitation copula in the validation period, unlike separate 1D temperature and precipitation corrections which, by construction, leave the copula unchanged. An unexpected result is that a relatively small number (<5) of temperature bins are required to achieve significant improvements in the copula. Results are similar for all stations.