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Weighting models according to their performance has been used to produce multimodel climate change projections. But the added value of model weighting for future projection is not always examined. Here we apply an imperfect model framework to evaluate the added value of model weighting in projecting summer temperature changes over China. Members of large-ensemble simulations by three climate models of different climate sensitivities are used as pseudo-observations for the past and the future. Performance of the models participating in the phase 6 of the Coupled Model Intercomparison Project (CMIP6) are evaluated against the pseudo-observations based on simulated historical climatology and trends in global, regional, and local temperatures to determine the model weights for future projection. The weighted projections are then compared with the pseudo-observations in the future period. We find that regional trend as a metric of model performance yields generally better skill for future projection, while past climatology as performance metric does not lead to a significant improvement to projection. Trend at the grid-box scale is also not a good performance indicator as small-scale trend is highly uncertain. For the model weighting to be effective, the metric for evaluating the model's performance must be relatable to future changes, with the response signal separable from internal variability. Projected summer warming based on model weighting is similar to that of unweighted projection but the 5th–95th-percentile uncertainty range of the weighted projection is 38% smaller with the reduction mainly in the upper bound, with the largest reduction appearing in southeast China. [ABSTRACT FROM AUTHOR]

In China, the topography, climate, ecology, hydrology, and human environment vary greatly from southeast to northwest, and a typical natural and social environmental transition zone (i.e., comprehensive transition zone) exists near the "Hu Huanyong line," which is a famous demographic dividing line in China, known as the Hu zone. Dry and wet climate changes in the Hu zone can have a significant impact on terrestrial ecosystems and hydrological conditions, ultimately affecting human–land relations. However, there is still a lack of clear understanding of environmental changes in the context of climate change in the Hu zone. Here, a quantitative analysis of climate change and its impacts on terrestrial hydrology and ecosystems from 1951 to 2020 is presented. The results showed that there exists a significant drying trend in the Hu zone and a dramatic decrease in terrestrial water storage (TWS), indicating that the environment has become worse. Conversely, from the perspective of significant greening, the environment has improved. This contradiction is mainly due to climate change dominating the depletion of TWS, while the increase in vegetation greenness is more driven by human activities including agricultural management and ecological restoration, offsetting to some extent the negative impact of water scarcity on vegetation growth. Significance Statement: The Hu zone is a transition zone between southeast and northwest China, which is a sensitive area under climate change as well as key region for coordinated development. The purpose of this study is to reveal the long-term climate change in the Hu zone and its impacts on hydrology and ecology. Our results indicate a significant drying trend in this zone over the last 70 years, which led to a substantial reduction in water storage. However, the vegetation coverage increased due to human activities. This study provides guidance for agricultural structure adjustment and ecological protection in transition zones. Future research should focus more on the assessment and risk management of dry/wet changes in climate transition areas around the world. [ABSTRACT FROM AUTHOR]

Observations and model simulations have confirmed that the higher percentiles of precipitation intensity of all wet days have increased with the local daily dewpoint temperature at the Clausius–Clapeyron (C-C) rate of 7% °C−1 using the binning technique over China. It is uncertain whether the binning scaling rates will remain constant in the future climate. Here, we use daily precipitation and dewpoint temperature (DPT) over China from 20 CMIP6 models to examine and project the change of binning scaling of precipitation extremes. The results of the study suggest that the multimodel ensemble median (MEM) generally captures the spatial distribution of binning scaling rates and binning curves in different climate zones across China. The binning scaling rates have stability in the future but with a large increasing trend (more than 30%) in southeast China in the far future (2061–2100) under the SSP5-8.5 scenario. CMIP6 models project that both the peak of extreme precipitation and the temperature for the plateau mountain zone will increase in the far future under the SSP5-8.5 scenario, implying that the peak structure does not provide an upper limit for future precipitation extremes. Significance Statement: The binning scaling rates describe the relationship between precipitation extremes and temperature based on their day-to-day variations. However, it is still unclear whether the binning scaling rates of daily precipitation extremes with daily temperature will change in the future. We use 20 CMIP6 models to project the scaling rates of precipitation extremes with dewpoint temperature over China in the future. The results show that the sensitivity of precipitation extremes to dewpoint temperature will generally be stable, except that southeast China shows an increasing trend of up to 30%. Moreover, the peak of extreme precipitation and the temperature will increase with warming in the plateau mountain zone. Our results have implications for precipitation prediction and risk assessment. [ABSTRACT FROM AUTHOR]