In applications of climate information, coarse-resolution climate projections commonly need to be downscaled to a finer grid. One challenge of this requirement is the modeling of sub-grid variability and the spatial and temporal dependence at the finer scale. Here, a post-processing procedure is proposed for temperature projections that addresses this challenge. The procedure employs statistical bias correction and stochastic downscaling in two steps. In a first step, errors that are related to spatial and temporal features of the first two moments of the temperature distribution at model scale are identified and corrected. Secondly, residual spacetime dependence at the finer scale is analyzed using a statistical model, from which realizations are generated and then combined with appropriate climate change signal to form the downscaled projection fields. Using a high-resolution observational gridded data product, the proposed approach is applied in a case study where projections of two regional climate models from the EURO-CORDEX ensemble are bias-corrected and downscaled to a 1 × 1 km grid in the Trøndelag area of Norway.A cross-validation study shows that the proposed procedure generates results that better reflect the marginal distributional properties of the data product and have better consistency in space and time than empirical quantile mapping.

Abstract. Climate change impact assessment related to floods, infrastructure networks, and water resource management applications requires realistic simulations of high-resolution gridded precipitation series under a future climate. This paper proposes to produce such simulations by combining a weather generator for high-resolution gridded daily precipitation, trained on a historical observation-based gridded data product, with coarser-scale climate change information obtained using a regional climate model. The climate change information can be added to various components of the weather generator, related to both the probability of precipitation as well as the amount of precipitation on wet days. The information is added in a transparent manner, allowing for an assessment of the plausibility of the added information. In a case study of nine hydrological catchments in central Norway with the study areas covering 1000–5500 km2, daily simulations are obtained on a 1 km grid for a period of 19 years. The method yields simulations with realistic temporal and spatial structures and outperforms empirical quantile delta mapping in terms of marginal performance.

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