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  • Source Publication: Geology, 43, 23‐26, doi:10.1130/G36179.1 Authors: Ullman, D.J., A.E. Carlson, A.N. LeGrande, A.K. Moore, F.S. Anslow, M. Caffee, K.M. Syverson, and J.M. Licciardi Publication Date: Nov 2015

    Establishing the precise timing for the onset of ice-sheet retreat at the end of the Last Glacial Maximum (LGM) is critical for delineating mechanisms that drive deglaciations. Uncertainties in the timing of ice-margin retreat and global ice-volume change allow a variety of plausible deglaciation triggers. Using boulder 10Be surface exposure ages, we date initial southern Laurentide ice-sheet (LIS) retreat from LGM moraines in Wisconsin (USA) to 23.0 ± 0.6 ka, coincident with retreat elsewhere along the southern LIS and synchronous with the initial rise in boreal summer insolation 24–23 ka. We show with climate-surface mass balance simulations that this small increase in boreal summer insolation alone is potentially sufficient to drive enhanced southern LIS surface ablation. We also date increased southern LIS retreat after ca. 20.5 ka likely driven by an acceleration in rising isolation. This near-instantaneous southern LIS response to boreal summer insolation before any rise in atmospheric CO2 supports the Milanković hypothesis of orbital forcing of deglaciations.

  • Source Publication: Climate Dynamics, 45, 7, 1713-1726 doi:10.1007/s00382‐014‐2423‐y Authors: Wan, H., X. Zhang, F.W. Zwiers and S.K. Min Publication Date: Oct 2015

    Using an optimal fingerprinting method and improved observations, we compare observed and CMIP5 model simulated annual, cold season and warm season (semi-annual) precipitation over northern high-latitude (north of 50°N) land over 1966–2005. We find that the multi-model simulated responses to the effect of anthropogenic forcing or the effect of anthropogenic and natural forcing combined are consistent with observed changes. We also find that the influence of anthropogenic forcing may be separately detected from that of natural forcings, though the effect of natural forcing cannot be robustly detected. This study confirms our early finding that anthropogenic influence in high-latitude precipitation is detectable. However, in contrast with the previous study, the evidence now indicates that the models do not underestimated observed changes. The difference in the latter aspect is most likely due to improvement in the spatial–temporal coverage of the data used in this study, as well as the details of data processing procedures.

  • Source Publication: Climate Symposium 2014 – Findings and Recommendations. Bulletin of the American Meteorological Society, 96, ES145–ES147, doi:10.1175/BAMS-D-15-00003.1 Authors: Asrar, G, S. Bony, O. Boucher, A. Busalacchi, A. Cazenave, M. Dowell, G. Flato, G. Hegerl, E. Källén, T. Nakajima, A. Ratier, R. Saunders, J. Slingo, B. Sohn, J. Schmetz, B. Stevens, P. Zhang and F. Zwiers Publication Date: Sep 2015

    The Climate Symposium 2014, organized by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) and the World Climate Research Programme (WCRP), was entitled “Climate Research and Earth Observation from Space—Climate Information for Decision Making.” Session topics revolved around the six Grand Science Challenges of the WCRP and addressed the specific need for, and role of, climate observations from space. Based on the presentations and discussions at the symposium, the Science Programme Committee identified main findings and recommendations, which are presented in this summary.

  • Source Publication: Weather and Climate Extremes special issue, 9, 47-56, doi:10.1016/j.wace.2015.04.001 Authors: Mueller, B., M.C. Hauser, C. Iles, R. Haque Rimi, F.W. Zwiers and H. Wan Publication Date: Sep 2015

    Human-induced increases in atmospheric greenhouse gas concentrations have led to rising global temperatures. Here we investigate changes in an annual temperature-based index, the growing season length, defined as the number of days with temperature above 5 °C. We show that over extratropical regions where wheat and maize are harvested, the increase in growing season length from 1956 to 2005 can be attributed to increasing greenhouse gas concentrations. Our analyses also show that climate change has increased the probability of extremely long growing seasons by a factor of 25, and decreased the probability of extremely short growing seasons. A lengthening of the growing season in regions with these mostly rain-fed crops could improve yields, provided that water availability does not become an issue. An expansion of areas with more than 150 days of growing season into the northern latitudes makes more land potentially available for planting wheat and maize. Furthermore, double-cropping can become an alternative to current practices in areas with very long growing seasons which are also shown to increase with a warming climate. These results suggest that there is a strong impact of anthropogenic climate change on growing season length. However, in some regions and with further exacerbated climate change, high temperatures may already be or may become a limiting factor for plant productivity.

  • Source Publication: Journal of Climate 28.17, 6938-6959, doi:10.1175/JCLI-D-14-00754.1. Authors: Cannon, A.J., S.R. Sobie and T.Q. Murdock Publication Date: Sep 2015

    Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. Although they are effective at removing historical biases relative to observations, it has been found that quantile mapping can artificially corrupt future model-projected trends. Previous studies on the modification of precipitation trends by quantile mapping have focused on mean quantities, with less attention paid to extremes. This article investigates the extent to which quantile mapping algorithms modify global climate model (GCM) trends in mean precipitation and precipitation extremes indices. First, a bias correction algorithm, quantile delta mapping (QDM), that explicitly preserves relative changes in precipitation quantiles is presented. QDM is compared on synthetic data with detrended quantile mapping (DQM), which is designed to preserve trends in the mean, and with standard quantile mapping (QM). Next, methods are applied to phase 5 of the Coupled Model Intercomparison Project (CMIP5) daily precipitation projections over Canada. Performance is assessed based on precipitation extremes indices and results from a generalized extreme value analysis applied to annual precipitation maxima. QM can inflate the magnitude of relative trends in precipitation extremes with respect to the raw GCM, often substantially, as compared to DQM and especially QDM. The degree of corruption in the GCM trends by QM is particularly large for changes in long period return values. By the 2080s, relative changes in excess of +500% with respect to historical conditions are noted at some locations for 20-yr return values, with maximum changes by DQM and QDM nearing +240% and +140%, respectively, whereas raw GCM changes are never projected to exceed +120%.

  • Source Publication: Weather and Climate Extremes, 9, 2-5, doi:10.1016/j.wace.2015.08.003 Authors: Seneviratne, S.I. and F.W. Zwiers Publication Date: Aug 2015

    This special issue of Weather and Climate Extremes (WACE) includes a series of articles initiated during the 2014 WCRP summer school on the “Attribution and Prediction of Extreme Events”. The two-week summer school took place from 21st July to 4th August 2014 at the International Center for Theoretical Physics (ICTP) in Trieste, Italy, and was organized in the context of the WCRP Grand Challenge on Extremes.

  • Source Publication: Journal of Hydrometeorology, 16, 1273–1292, doi:http://dx.doi.org/10.1175/JHM-D-14-0167.1 Authors: Shrestha, R.R., M.A. Schnorbus and A.J. Cannon Publication Date: Jun 2015

    Recent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical climate model–driven hydrologic prediction system (CM-HPS), based on an ensemble of statistically downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS). For comparison, historical and future climate traces–driven ensemble streamflow prediction (ESP) was employed. The Variable Infiltration Capacity model (VIC) hydrologic model setup for the Fraser River basin, British Columbia, Canada, was used as a test bed for the two systems. In both cases, results revealed limited precipitation prediction skill. For streamflow prediction, the ESP approach has very limited or no correlation skill beyond the months influenced by initial hydrologic conditions, while the CM-HPS has moderately better correlation skill, attributable to the enhanced temperature prediction skill that results from CanSIPS’s ability to predict El Niño–Southern Oscillation (ENSO) and its teleconnections. The root-mean-square error, bias, and categorical skills for the two methods are mostly similar. Hydrologic modeling uncertainty also affects the prediction skill, and in some cases prediction skill is constrained by hydrologic model skill. Overall, the CM-HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skillful hydrologic predictions.

  • Source Publication: Journal of Hydrometeorology, 16, 1273–1292, doi:10.1175/JHM‐D‐14‐0167.1 Authors: Shrestha, R.R., M.A. Schnorbus and A.J. Cannon Publication Date: Jun 2015

    Recent improvements in forecast skill of the climate system by dynamical climate models could lead to improvements in seasonal streamflow predictions. This study evaluates the hydrologic prediction skill of a dynamical climate model–driven hydrologic prediction system (CM-HPS), based on an ensemble of statistically downscaled outputs from the Canadian Seasonal to Interannual Prediction System (CanSIPS). For comparison, historical and future climate traces–driven ensemble streamflow prediction (ESP) was employed. The Variable Infiltration Capacity model (VIC) hydrologic model setup for the Fraser River basin, British Columbia, Canada, was used as a test bed for the two systems. In both cases, results revealed limited precipitation prediction skill. For streamflow prediction, the ESP approach has very limited or no correlation skill beyond the months influenced by initial hydrologic conditions, while the CM-HPS has moderately better correlation skill, attributable to the enhanced temperature prediction skill that results from CanSIPS’s ability to predict El Niño–Southern Oscillation (ENSO) and its teleconnections. The root-mean-square error, bias, and categorical skills for the two methods are mostly similar. Hydrologic modeling uncertainty also affects the prediction skill, and in some cases prediction skill is constrained by hydrologic model skill. Overall, the CM-HPS shows potential for seasonal streamflow prediction, and further enhancements in climate models could potentially to lead to more skillful hydrologic predictions

  • Source Publication: Journal of Hydrologic Engineering, 04015043, doi: 10.1061/(ASCE)HE.1943-5584.0001250 Authors: Najafi M.R. and H. Moradkhani Publication Date: Jun 2015

    Various hydrologic models with different complexities have been developed to represent the characteristics of river basins, improve streamflow forecasts such as seasonal volumetric flow predictions, and meet other demands from different stakeholders. Because no single hydrologic model is able to perfectly simulate the observed flow, multimodel combination techniques are developed to combine forecasts obtained from different models and to quantify the uncertainties with the goal of improving upon single-model performance. In this study, a comprehensive set of multimodel ensemble averaging techniques with varying complexities are investigated for operational forecasting over four river basins in the Western United States. Ensemble merging models are divided into three categories of simple, intermediate, and complex, and comparison is made between each class by using a bootstrap approach. Analysis suggests that model combination effectively improves most of the individual seasonal forecasts and can outperform the best forecast model. Simple average, median, Bates-Granger, constrained linear regression, and Bayesian model averaging optimized by expectation maximization showed better results compared with other methods over three basins. For the Rogue River basin, the intermediate and complex models outperformed most of the individual forecasts and the simple methods. Multimodeling techniques based on information criteria showed similar performances.

  • Source Publication: Journal of Hydrology, 525, 352-361, doi: 10.1016/j.jhydrol.2015.03.045 Authors: Najafi M.R. and H. Moradkhani Publication Date: Jun 2015

    In this study multi-model ensemble analysis of extreme runoff is performed based on eight regional climate models (RCMs) provided by the North American Regional Climate Change Assessment Program (NARCCAP). Hydrologic simulation is performed by driving the Variable Infiltration Capacity (VIC) model over the Pacific Northwest region, for historical and future time periods. Extreme event analysis is then conducted using spatial hierarchical Bayesian modeling (SHB). Ensemble merging of extreme runoff is carried out using Bayesian Model Averaging (BMA) in which spatially distributed weights corresponding to each regional climate model are obtained. Comparison of the residuals before and after the multi-model combination shows that the merged signal generally outperforms the best individual signal. The climate model simulations show close performance regarding maximum and minimum temperature and wind speed, however, the differences are more pronounced for precipitation and runoff. Between-model variances increase for the future time series compared to the historical ones indicating larger uncertainties in climate change projections. The combined model is then used to predict projected seasonal runoff extremes and compare them with historical simulations. Ensemble average results suggest that seasonal extreme runoff will increase in most regions in particular the Rockies and west of the Cascades.

  • Source Publication: Climate Dynamics,doi:10.1007/s00382-015-2674-2 Authors: Kim, Y.H., S.K. Min , X. Zhang, F. Zwiers, L.V. Alexander, M.G. Donat and Y.S. Tung Publication Date: May 2015

    An attribution analysis of extreme temperature changes is conducted using updated observations (HadEX2) and multi-model climate simulation (CMIP5) datasets for an extended period of 1951–2010. Compared to previous HadEX/CMIP3-based results, which identified human contributions to the observed warming of extreme temperatures on global and regional scales, the current results provide better agreement with observations, particularly for the intensification of warm extremes. Removing the influence of two major modes of natural internal variability (the Arctic Oscillation and Pacific Decadal Oscillation) from observations further improves attribution results, reducing the model-observation discrepancy in cold extremes. An optimal fingerprinting technique is used to compare observed changes in annual extreme temperature indices of coldest night and day (TNn, TXn) and warmest night and day (TNx, TXx) with multi-model simulated changes that were simulated under natural-plus-anthropogenic and natural-only (NAT) forcings. Extreme indices are standardized for better intercomparisons between datasets and locations prior to analysis and averaged over spatial domains from global to continental regions following a previous study. Results confirm previous HadEX/CMIP3-based results in which anthropogenic (ANT) signals are robustly detected in the increase in global mean and northern continental regional means of the four indices of extreme temperatures. The detected ANT signals are also clearly separable from the response to NAT forcing, and results are generally insensitive to the use of different model samples as well as different data availability.

  • Source Publication: Climate Dynamics, doi:10.1007/s00382-015-2642-x. Authors: Seiler, C. and F.W. Zwiers Publication Date: May 2015

    Extratropical explosive cyclones are rapidly intensifying low pressure systems with severe wind speeds and heavy precipitation, affecting livelihoods and infrastructure primarily in coastal and marine environments. This study evaluates how well the most recent generation of climate models reproduces extratropical explosive cyclones in the Northern Hemisphere for the period 1980–2005. An objective-feature tracking algorithm is used to identify and track cyclones from 25 climate models and three reanalysis products. Model biases are compared to biases in the sea surface temperature (SST) gradient, the polar jet stream, the Eady growth rate, and model resolution. Most models accurately reproduce the spatial distribution of explosive cyclones when compared to reanalysis data (R = 0.94), with high frequencies along the Kuroshio Current and the Gulf Stream. Three quarters of the models however significantly underpredict explosive cyclone frequencies, by a third on average and by two thirds in the worst case. This frequency bias is significantly correlated with jet stream speed in the inter-model spread (R ≥ 0.51), which in the Atlantic is correlated with a negative meridional SST gradient (R = −0.56). The importance of the jet stream versus other variables considered in this study also applies to the interannual variability of explosive cyclone frequency. Furthermore, models with fewer explosive cyclones tend to underpredict the corresponding deepening rates (R ≥ 0.88). A follow-up study will assess the impacts of climate change on explosive cyclones, and evaluate how model biases presented in this study affect the projections.

  • Source Publication: Climate Dynamics, doi:10.1007/s00382-015-2791-y Authors: Seiler, C. and, F.W. Zwiers Publication Date: May 2015

    Explosive cyclones are rapidly intensifying low pressure systems generating severe wind speeds and heavy precipitation primarily in coastal and marine environments. This study presents the first analysis on how explosive cyclones respond to climate change in the extratropics of the Northern Hemisphere. An objective-feature tracking algorithm is used to identify and track cyclones from 23 CMIP5 climate models for the recent past (1981–1999) and future (2081–2099). Explosive cyclones are projected to shift northwards by about 2.2∘ latitude on average in the northern Pacific, with fewer and weaker events south of 45∘N, and more frequent and stronger events north of this latitude. This shift is correlated with a poleward shift of the jet stream in the inter-model spread (R=0.56). In the Atlantic, the total number of explosive cyclones is projected to decrease by about 17 % when averaging across models, with the largest changes occurring along North America’s East Coast. This reduction is correlated with a decline in the lower-tropospheric Eady growth rate (R=0.51), and is stronger for models with smaller frequency biases (R=−0.65). The same region is also projected to experience a small intensification of explosive cyclones, with larger vorticity values for models that predict stronger increases in the speed of the jet stream (R=0.58). This strengthening of the jet stream is correlated with an enhanced sea surface temperature gradient in the North Atlantic (R=−0.63). The inverse relationship between model bias and projection, and the role of model resolution are discussed.

  • Source Publication: Hydrology and Earth System Sciences Discussions, 12, 6, doi: 10.5194/hessd-12-6179-2015 Authors: Werner, A.T. and A.J. Cannon Publication Date: May 2015

    Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e., correlation tests) and distributional properties (i.e., tests for equality of probability distributions). Outputs from seven downscaling methods – bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), climate imprint delta method (CI), and bias corrected CI (BCCI) – are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3 day peak flow and 7 day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational datasets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational dataset. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7 day low flow events, regardless of reanalysis or observational dataset. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis datasets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical datasets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.

  • Source Publication: Nature Geoscience 8, 372–377, doi:10.1038/ngeo2407 Authors: Clarke, G.K.C., A.H. Jarosch, F.S. Anslow, V. Radic and B. Menounos Publication Date: May 2015

    Retreat of mountain glaciers is a significant contributor to sea-level rise and a potential threat to human populations through impacts on water availability and regional hydrology. Like most of Earth’s mountain glaciers, those in western North America are experiencing rapid mass loss. Projections of future large-scale mass change are based on surface mass balance models that are open to criticism, because they ignore or greatly simplify glacier physics. Here we use a high-resolution regional glaciation model, developed by coupling physics-based ice dynamics with a surface mass balance model, to project the fate of glaciers in western Canada. We use twenty-first-century climate scenarios from an ensemble of global climate models in our simulations; the results indicate that by 2100, the volume of glacier ice in western Canada will shrink by 70 ± 10% relative to 2005. According to our simulations, few glaciers will remain in the Interior and Rockies regions, but maritime glaciers, in particular those in northwestern British Columbia, will survive in a diminished state. We project the maximum rate of ice volume loss, corresponding to peak input of deglacial meltwater to streams and rivers, to occur around 2020–2040. Potential implications include impacts on aquatic ecosystems, agriculture, forestry, alpine tourism and water quality.

  • Authors: Shrestha, R.R., M.A. Schnorbus, A.J. Cannon and F.W. Zwiers Publication Date: May 2015
  • Source Publication: International Journal of Climatology, doi: 10.1002/joc.4361 Authors: Salimun, E., F. Tangang, L. Juneng, F. W. Zwiers and W. J. Merryfield Publication Date: May 2015

    This study evaluates the forecast skill of the fourth version of the Canadian coupled ocean–atmosphere general circulation model (CanCM4) and its model output statistics (MOS) to forecast the seasonal rainfall in Malaysia, particularly during early (October–November–December) and late (January–February–March) winter monsoon periods. CanCM4 is the latest component of the Canadian Seasonal to Inter-annual Prediction System (CanSIPS), which is a multi-seasonal climate prediction system developed particularly for Canada but applicable globally. Generally, CanCM4's skill in reproducing the climatology during winter is not as good as in other seasons because of the model's inability to simulate the regional synoptic circulations over the western Maritime Continent. In particular, the model fails to forecast the cold surges and Borneo vortex circulations that play critical roles in moisture horizontal advection. Moreover, its forecast skill during the early winter monsoon period is poorer than during the late period. Interestingly, forecast skill is enhanced when MOS models are applied as the MOS utilizes the predictive signals in the quasi-global predictors from the CanCM4 forecast system. The predictability can be traced to the conventional El Niño–Southern Oscillation (ENSO) and ENSO Modoki signals that are present in the CanCM4 forecast MOS predictor fields. The quasi-global sea-surface temperature and quasi-global sea-level pressure fields are found to be the most useful predictors. Interestingly, CanCM4 forecast signals associated with the Indian Ocean Dipole also contribute to the skill. Skill enhancement is particularly significant for northern Borneo during early monsoon periods in medium- and long-lead forecasts when the CanCM4 has minimal direct skill in the region.

  • Source Publication: International Journal of Climatology, doi: 0.1002/joc.4361 Authors: Salimun, E., F. Tangang, L. Juneng, F.W. Zwiers and W..J. Merryfield Publication Date: May 2015

    This study evaluates the forecast skill of the fourth version of the Canadian coupled ocean–atmosphere general circulation model (CanCM4) and its model output statistics (MOS) to forecast the seasonal rainfall in Malaysia, particularly during early (October–November–December) and late (January–February–March) winter monsoon periods. CanCM4 is the latest component of the Canadian Seasonal to Inter-annual Prediction System (CanSIPS), which is a multi-seasonal climate prediction system developed particularly for Canada but applicable globally. Generally, CanCM4's skill in reproducing the climatology during winter is not as good as in other seasons because of the model's inability to simulate the regional synoptic circulations over the western Maritime Continent. In particular, the model fails to forecast the cold surges and Borneo vortex circulations that play critical roles in moisture horizontal advection. Moreover, its forecast skill during the early winter monsoon period is poorer than during the late period. Interestingly, forecast skill is enhanced when MOS models are applied as the MOS utilizes the predictive signals in the quasi-global predictors from the CanCM4 forecast system. The predictability can be traced to the conventional El Niño–Southern Oscillation (ENSO) and ENSO Modoki signals that are present in the CanCM4 forecast MOS predictor fields. The quasi-global sea-surface temperature and quasi-global sea-level pressure fields are found to be the most useful predictors. Interestingly, CanCM4 forecast signals associated with the Indian Ocean Dipole also contribute to the skill. Skill enhancement is particularly significant for northern Borneo during early monsoon periods in medium- and long-lead forecasts when the CanCM4 has minimal direct skill in the region.

  • Source Publication: Geophysical Research Letters, 41, 10, 3586‐3593, doi:10.1002/2014GL059586 Authors: Kumar, S., P. Dirmeyer and J. Kinter III Publication Date: May 2015

    Typically, sub-seasonal to intra-annual climate forecasts are based on ensemble mean (EM) predictions. The EM prediction provides only a part of the information available from the ensemble forecast. Here we test the null hypothesis that the observations are randomly distributed about the EM predictions using a new metric that quantifies the distance between the EM predictions from the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 (CFSv2) and the observations represented by CFSv2 Reanalysis. The null hypothesis cannot be rejected in this study. Hence, we argue that the higher order statistics such as ensemble standard deviation are also needed to describe the forecast. We also show that removal of systematic errors that are a function of the forecast initialization month and lead time is a necessary pre-processing step. Finally, we show that CFSv2 provides useful ensemble climate forecasts from 0 to 9 month lead time in several regions.

  • Source Publication: Theoretical and Applied Climatology, 120, 1, 377-390, doi:10.1007/s00704‐014‐1157‐4 Authors: Farajzadeh, M., R. Oji, A.J. Cannon, Y. Ghavidel, and A.R. Massah Publication Date: Apr 2015

    Seven single-site statistical downscaling methods for daily temperature and precipitation, including four deterministic algorithms [analog model (ANM), quantile mapping with delta method extrapolation (QMD), cumulative distribution function transform (CDFt), and model-based recursive partitioning (MOB)] and three stochastic algorithms [generalized linear model (GLM), Conditional Density Estimation Network Creation and Evaluation (CaDENCE), and Statistical Downscaling Model–Decision Centric (SDSM–DC] are evaluated at nine stations located in the mountainous region of Iran’s Midwest. The methods are of widely varying complexity, with input requirements that range from single-point predictors of temperature and precipitation to multivariate synoptic-scale fields. The period 1981–2000 is used for model calibration and 2001–2010 for validation, with performance assessed in terms of 27 Climate Extremes Indices (CLIMDEX). The sensitivity of the methods to large-scale anomalies and their ability to replicate the observed data distribution in the validation period are separately tested for each index by Pearson correlation and Kolmogorov–Smirnov (KS) tests, respectively. Combined tests are used to assess overall model performances. MOB performed best, passing 14.5 % (49.6 %) of the combined (single) tests, respectively, followed by SDSM, CaDENCE, and GLM [14.5 % (46.5 %), 13.2 % (47.1 %), and 12.8 % (43.2 %), respectively], and then by QMD, CDFt, and ANM [7 % (45.7 %), 4.9 % (45.3 %), and 1.6 % (37.9 %), respectively]. Correlation tests were passed less frequently than KS tests. All methods downscaled temperature indices better than precipitation indices. Some indices, notably R20, R25, SDII, CWD, and TNx, were not successfully simulated by any of the methods. Model performance varied widely across the study region.

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