A new technique for statistically downscaling climate model simulations of daily temperature and precipitation is introduced and demonstrated over the western United States. The localized constructed analogs (LOCA) method produces downscaled estimates suitable for hydrological simulations using a multiscale spatial matching scheme to pick appropriate analog days from observations. First, a pool of candidate observed analog days is chosen by matching the model field to be downscaled to observed days over the region that is positively correlated with the point being downscaled, which leads to a natural independence of the downscaling results to the extent of the domain being downscaled. Then, the one candidate analog day that best matches in the local area around the grid cell being downscaled is the single analog day used there. Most grid cells are downscaled using only the single locally selected analog day, but locations whose neighboring cells identify a different analog day use a weighted combination of the center and adjacent analog days to reduce edge discontinuities. By contrast, existing constructed analog methods typically use a weighted average of the same 30 analog days for the entire domain. By greatly reducing this averaging, LOCA produces better estimates of extreme days, constructs a more realistic depiction of the spatial coherence of the downscaled field, and reduces the problem of producing too many light-precipitation days. The LOCA method is more computationally expensive than existing constructed analog techniques, but it is still practical for downscaling numerous climate model simulations with limited computational resources.

Abstract. When applying a quantile mapping-based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961-1980 and validating it during a test period of 1981-1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values.

When applying a quantile-mapping based bias correction to daily temperature extremes simulated by a global climate model (GCM), the transformed values of maximum and minimum temperatures are changed, and the diurnal temperature range (DTR) can become physically unrealistic. While causes are not thoroughly explored, there is a strong relationship between GCM biases in snow albedo feedback during snowmelt and bias correction resulting in unrealistic DTR values. We propose a technique to bias correct DTR, based on comparing observations and GCM historic simulations, and combine that with either bias correcting daily maximum temperatures and calculating daily minimum temperatures or vice versa. By basing the bias correction on a base period of 1961–1980 and validating it during a test period of 1981–1999, we show that bias correcting DTR and maximum daily temperature can produce more accurate estimations of daily temperature extremes while avoiding the pathological cases of unrealistic DTR values

The general circulation model (GCM) experiments conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) [Taylor et al., 2012], which is being conducted in preparation for the Intergovernmental Panel on Climate Change's Fifth Assessment Report, provide fundamental data sets for assessing the effects of global climate change. However, efforts to assess regional or local effects of the projected changes in climate are often impeded by the coarse spatial resolution of the GCM outputs, as well as potential local or regional biases in GCM outputs [Fowler et al., 2007].

During the early Paleogene, climate in continental interiors is thought to have been warmer and more equable than today, but estimates of seasonal temperature variations during this period are limited. Global and regional climate models of the Paleogene predict cooler temperatures for continental interiors than are implied by proxy data and predict a seasonal range of temperature that is similar to today. Here, we present a record of summer temperatures derived from carbonate clumped isotope thermometry of paleosol carbonates from Paleogene deposits in the Bighorn Basin, Wyoming (United States). Our summer temperature estimates are ~18 °C greater than mean annual temperature estimated from analysis of fossil leaves. When coupled, these two records yield a seasonal range of temperature similar to that in the region today, with winter temperatures that are near freezing. These data are consistent with our high-resolution climate model output for the Early Eocene in the Bighorn Basin. We suggest that temperatures in continental interiors during the early Paleogene greenhouse were warmer in all seasons, but not more equable than today. If generally true, this removes one of the long-standing paradoxes in our understanding of terrestrial climate dynamics under greenhouse conditions.