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Significant increases in temperature and precipitation due to global warming affect socioeconomics. Accurate analysis is needed for future temperature and precipitation variations across the Yangtze River basin (YRB). A novel quantile delta-mapped spatial disaggregation (QDMSD) approach was developed in this study to analyze temperature and precipitation changes for the first time. The evaluation results show that the QDMSD has a similar performance in simulating temperature with the bias correction and spatial disaggregation (BCSD) model, while it shows improvement in reproducing precipitation. Projections indicate the annual-mean temperature will increase from 2020 to 2080 under shared socioeconomic pathway (SSP) scenarios SSP2-4.5 and SSP5-8.5. The projected temperature obtained from five downscaled GCMs has the smallest range of differences in summer. Conversely, annual-mean temperatures significantly decrease from 2081 to 2100 under SSP2-4.5. In terms of spatial distribution characteristics, most of the positive changes tend to expand across the YRB. The annual-mean precipitation will increase from 2020 to 2080 but decrease from 2081 to 2100 under SSP2-4.5 over the YRB. In terms of spatial distribution, precipitation in the southeast region of the YRB will increase, and the maximum variations in precipitation will occur downstream of the YRB. The QDMSD method reproduces observed precipitation trends well and enhances simulation accuracy in the YRB. For projected temperature, there will be a widespread increase across the YRB; for projected precipitation, significant increases will occur in the eastern YRB. These findings support policymaking to address potential risks from temperature and precipitation changes across multiple sectors (e.g., agriculture and industry). [ABSTRACT FROM AUTHOR]

The systematic bias of the estimated average temperature using daily Tmax and Tmin records relative to the standard average temperature of four time-equidistant observations and its effect on the estimated trend of long-term temperature change have not been well understood. This paper attempts to evaluate the systematic bias across mainland China using the daily data of national observational stations. The results revealed that the positive bias of annual mean temperature was large, reaching 0.58°C nationally on average; regional average bias was lowest in the northwest arid region and highest in the Qinghai–Tibetan Plateau; the bias was low in spring and summer and high in autumn and winter, reaching its lowest point in mid- and late May and highest point in early November. Furthermore, the bias showed a significant upward trend in the past 50 years, with a rising rate of 0.021°C (10 yr)−1, accounting for about 12% of the overall warming as estimated from the data of the observational network; the largest positive trend bias was found in the northwest arid region, while the east monsoon region experienced the smallest change; the most remarkable increase of the bias occurred after early 1990s. These results indicate that the customarily applied method to calculate daily and monthly mean temperature using Tmax and Tmin significantly overestimates the climatological mean and the long-term trend of surface air temperature in mainland China. [ABSTRACT FROM AUTHOR]

In this paper, wintertime precipitation from a variety of observational datasets, regional climate models (RCMs), and general circulation models (GCMs) is averaged over the state of California and compared. Several averaging methodologies are considered and all are found to give similar values when the model grid spacing is less than 3°. This suggests that California is a reasonable size for regional intercomparisons using modern GCMs. Results show that reanalysis-forced RCMs tend to significantly overpredict California precipitation. This appears to be due mainly to the overprediction of extreme events; RCM precipitation frequency is generally underpredicted. Overprediction is also reflected in wintertime precipitation variability, which tends to be too high for RCMs on both daily and interannual scales. Wintertime precipitation in most (but not all) GCMs is underestimated. This is in contrast to previous studies based on global blended gauge-satellite observations, which are shown here to underestimate precipitation relative to higher-resolution gauge-only datasets. Several GCMs provide reasonable daily precipitation distributions, a trait that does not seem to be tied to model resolution. The GCM daily and interannual variabilities are generally underpredicted. [ABSTRACT FROM AUTHOR]

Public health planning needs the support of evidence-based information on current and future climate, which could be used by health professionals and decision makers to better understand and respond to the health impacts of extreme heat. Climate models provide information regarding the expected increase in temperatures and extreme heat events with climate change and can help predict the severity of future health impacts, which can be used in the public health sector for the development of adaptation strategies to reduce heat-related morbidity and mortality. This study analyzes the evolution of extreme temperature indices specifically defined to characterize heat events associated with health risks, in the context of a changing climate. The analysis is performed by using temperature projections from the Canadian Regional Climate Model. A quantile-based statistical correction is applied to the projected temperatures, in order to reduce model biases and account for the representativeness error. Moreover, generalized Pareto distributions are used to extend the temperature distribution upper tails and extrapolate the statistical correction to extremes that are not observed in the present but that might occur in the future. The largest increase in extreme daytime temperatures occurs in southern Manitoba, Canada, where the already overly dry climate and lack of soil moisture can lead to an uncontrolled enhancement of hot extremes. The occurrence of warm nights and heat waves, on the other hand, is already large and will increase substantially in the communities of the Great Lakes region, characterized by a humid climate. Impact and adaptation studies need to account for the temperature variability due to local effects, since it can be considerably larger than the model natural variability. [ABSTRACT FROM AUTHOR]

Over the next 10-50 years, policy makers in the southwestern United States are faced with complex planning and policy issues associated with increasing water and energy demand as a result of warmer temperatures and reduced availability of water, compounded by continued rapid population growth and economic development. This study uses a top-down, end-to-end approach consisting of dynamical downscaling, a novel bias-correction technique, and custom-developed decision-aid tools to assess regional climate changes in the Southwest and to derive decision aids that are based on direct communication with the planners at four military installations in the region. Dynamical downscaling is performed with the Weather Research and Forecasting model driven by the National Centers for Environmental Prediction reanalysis and the Max Planck Institute for Meteorology's ECHAM5 general circulation model for two time periods: current (2000s) and future (2030s). A unique two-stage bias correction is developed to adjust current and future hourly temperature and precipitation to be consistent with historical reference data. The authors' assessment of regional climate change, which is based on downscaled bias-corrected fields, points to a dryer and warmer future climate in the Southwest. The energy-usage modeling produced a statistically significant increase in natural gas consumption and a possible decrease in electricity usage in two military installations in Colorado, which is a direct consequence of decrease/increase in heating/cooling degree-days resulting from warmer temperatures in the future. In addition, the results indicate an increasing number of oppressive heat days in the future, which may impact long-term planning practices with respect to heat-stress control and heat-casualty management. [ABSTRACT FROM AUTHOR]