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Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station–RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS.

Climate impact assessment and decision making in the light of projected future climate change require accurate and robust climate scenarios at the local scale. The latter are targeted by statistical bias correction (BC) and downscaling of climate model output. A nowadays well‐established technique is quantile mapping (QM). Here, we apply several different implementations of empirical QM, a parametric (and computationally more expensive) QM method for daily precipitation, and a mean‐BC to an ensemble of regional climate scenarios over the topographically structured terrain of Switzerland. The performance of these methods in the current climate and their long‐term stability are analysed with respect to distributional and temporal statistics as well as climate impact indices for daily temperature and precipitation. We select an optimal QM implementation, study its effect on the inter‐variable consistency, and compare its climate change signal (CCS) to that of a delta‐change (DC) method. The results demonstrate that QM effectively reduces raw model biases. The most important improvements are corrected magnitudes and, for precipitation, also wet‐day frequencies. The quantile‐dependent bias removal is superior to the mean‐BC with respect to distribution‐tail statistics. Temporal statistics and climate impact indices are also improved. There is no performance benefit from the parametric QM method. The selected empirical QM implementation substantially improves the joint temperature–precipitation distribution and maintains the temperature–precipitation cross‐correlation function as represented by raw climate model data. The CCS analysis reveals the superiority of QM over the DC method with respect to distribution‐tail characteristics and temporal statistics. This work reveals empirical QM as a reliable and stable method for BC and downscaling from state‐of‐the‐art regional climate models to local weather stations over alpine terrain, confirming and expanding previous studies.

East Asia has experienced strong warming since the 1960s accompanied by an increased frequency of heat waves and shrinking glaciers over the Tibetan Plateau and the Tien Shan. Here, we place the recent warmth in a long-term perspective by presenting a new spatially resolved warm-season (May-September) temperature reconstruction for the period 1–2000 CE using 59 multiproxy records from a wide range of East Asian regions. Our Bayesian Hierarchical Model (BHM) based reconstructions generally agree with earlier shorter regional temperature reconstructions but are more stable due to additional temperature sensitive proxies. We find a rather warm period during the first two centuries CE, followed by a multi-century long cooling period and again a warm interval covering the 900–1200 CE period (Medieval Climate Anomaly, MCA). The interval from 1450 to 1850 CE (Little Ice Age, LIA) was characterized by cooler conditions and the last 150 years are characterized by a continuous warming until recent times. Our results also suggest that the 1990s were likely the warmest decade in at least 1200 years. The comparison between an ensemble of climate model simulations and our summer reconstructions since 850 CE shows good agreement and an important role of internal variability and external forcing on multi-decadal time-scales.