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This paper examines the impact of extreme precipitation on Chinese industrial output and its total factor productivity (TFP) using data from the annual survey of industrial firms (ASIF) and daily weather data. The findings demonstrate a significant reduction in industrial firm output as a result of precipitation, with extreme daily precipitation decreasing output by approximately 1.22%. We provide an explanation for this precipitation-industrial output relationship from four perspectives: per capita wages, labor productivity, inventory, and depreciation. The effects of extreme precipitation are more pronounced in high-income and high-precipitation regions, as well as among foreign firms and low-technology firms. Our research suggests that extreme daily precipitation poses a serious obstacle to the development of the manufacturing industry. These results are crucial for understanding the economic impact and concealed costs of climate change, highlighting that extreme precipitation should be recognized as one of the primary sources of damage associated with climate change in the future. [ABSTRACT FROM AUTHOR]

This paper matched the data for all non-financial listed firms in China from 1990 to 2017 with weather data and used temperature and precipitation to represent climate change. Using daily temperature and daily precipitation data, we constructed temperature bins and precipitation bins. This study presents a potential first empirical analysis that applies the risk perception theory to comprehensively examine the impacts of daily temperature and daily precipitation on firms' debt financing costs. Compared to a day with a suitable temperature, a day with temperatures above 30 °C increases firms' debt financing costs by 0.2411%. Compared to a sunny day, a day with torrential rain increases firms' financing costs by 0.2697%. Approximately $122.13 million and $20.49 million in extra costs were incurred across all firms in 2017. We conducted a series of heterogeneity tests. We found that the negative impacts of climate change on debt financing costs are greater for firms that investor considers more vulnerable. Finally, we found two possible mechanisms. First, from the firm's subjective initiative perspective, firms that respond proactively during seasons of high temperatures and heavy rains can reduce the negative impacts. Second, adverse weather caused by climate change may also increase firms' debt financing costs by blocking travel. These results enrich the literature on firms' debt financing costs and contribute to the understanding of climate change's impact on firms. [Display omitted] • An extra high-temperature day increases firms' debt financing cost by 0.2411%. • An extra heavy-rain day increases firms' debt financing costs by 0.2697%. • The negative impacts will be greater for those firms that investor considers more vulnerable. • Firms' response proactively can reduce the negative impacts. • Climate change also may increase firms' debt financing costs by blocking travel. [ABSTRACT FROM AUTHOR]

Loess Plateau has great uncertainty on drought occurrence due to climate change. This paper analyzes the evolution of precipitation, potential evapotranspiration and standardized precipitation evapotranspiration index (SPEI) based on the Coupled Model Inter-comparison Project Phase 5 (CMIP5) data and regional downscaling model (RegCM4.0). Results indicate that, under RCP2.6 Scenario, the precipitation will increase significantly (5% confidence level) at the rate of 16.40 mm/10 a. However, the potential evapotranspiration is showing non-significant decreasing trend at the rate of 2.16 mm/10 a. Moreover, the SPEI will decrease in the south and northernmost area and increase in the central northern area of Loess Plateau. Under RCP8.5 Scenario, the precipitation will increase significantly (5% confidence level) at the rate of 19.12 mm/10 a. The potential evapotranspiration will non-significantly decrease at the rate of 2.16 mm/10 a and the SPEI is showing increasing trend almost in the whole Loess Plateau. Generally, Loess Plateau is becoming wetter in the central part under RCP2.6 Scenario and the wet area will be enlarged to almost the whole plateau under RCP8.5 Scenario. Based on the results, the water resources will increase under global warming, which may alleviate the water scarcity issue in the Loess Plateau. [ABSTRACT FROM AUTHOR]

Summary The climate elasticity of runoff is an important indicator that is used to quantify the relationship between changes in runoff and changes in climate variables. It is a function of both climate and catchment characteristics. Recently, Yang and Yang (2011) proposed an analytical derivation of climate elasticity (YY2011), in which a parameter n was used to represent the impact of the catchment characteristics. In China, both climate and catchment characteristics have large spatial variations. To understand the spatial variation of hydrologic response to climate change, this paper divided China into 210 catchments, further calculated the parameter n , and then estimated the climate elasticity and evaluated the contribution of climate change to runoff for each catchment. The results show that n ranges from 0.4 to 3.8 (with a mean of 1.3 and a standard deviation of 0.6), which has a logarithmic relationship with catchment slope; the precipitation elasticity ranges from 1.1 to 4.8 (with a mean of 1.9 and a standard deviation of 0.6), which shows a large regional variation, smaller values (1.1–2.0) mainly appearing in Southern China, the Songhua River basin and the Northwest, and larger values (2.1–4.8) mainly appearing in the Hai River basin, the Liao River basin and the Yellow River basin. In addition, climate contribution to runoff exhibits a large regional variation, the largest positive values (1.1–3.1%/a) occurring in the Northwest, the largest negative values (−1.0 to −0.5%/a) occurring in the Hai River basin and the middle reach of the Yellow River basin. In theory, the YY2011 method is a first-order approximation. The approximation underestimates the precipitation ( P ) contribution to runoff when P increases and overestimates that when P decreases, and the relative error has a median of ∼3% and a maximum of ∼20% when 10% precipitations change in those catchments of China. [ABSTRACT FROM AUTHOR]

Quantifying the impacts of future climate change on the ecosystem and hydrology is very important to ecohydrology research. In this paper, the vegetation model BIOME BioGeochemicalCycles (BIOME-BGC) and the Hydrological Simulation Program-FORTRAN (HSPF) are combined to investigate the potential impact of climate change on the hydrology and ecosystems of the Guishui River Basin in Beijing China, under two emission scenarios A2 and B2 of the future period 2010–2099. The projected daily maximum and minimum air temperatures, precipitation and evapotranspiration ( ET ) of HadCM3 are downscaled on to local meteorological stations using the SDSM (Statistical Downscaling Model). The model calibration and validation results are in accord with observed data of surface runoff and Net Primary Productivity (NPP). Average climate projections based on two emission scenarios were used in simulations to assess future ecohydrological responses in the Guishui River basin. Also, the uncertainties in climate change impacts are discussed in detail. The predicted daily maximum and minimum air temperatures and evapotranspiration show a reasonable upward trend, while daily precipitation and surface runoff generally show a downward trend in the next 90 years. The changing rates of daily maximum and minimum temperatures, precipitation, evapotranspiration and surface runoff are 0.46 °C, 0.45 °C, −3.68 mm/year, 15.83 mm/year and −0.05 m 3 /s (0.26 °C, 0.26 °C, −4.99 mm/year, 9.36 mm/year and −0.04 m 3 /s) per decade under A2(B2) scenarios, respectively. The results also show that global warming impacts would decrease annual discharge and increase annual NPP (at the rate of 10–30%). These results present a serious challenge for water and land management in the basin and will provide support for regional water management. [ABSTRACT FROM AUTHOR]

This paper explored the projected changes in precipitation, maximum temperature (T max), and minimum temperature (T min) over the Yangtze River Basin (YRB) based on 23 Global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). The Empirical Quantile Mapping (EQM) approach was firstly applied to correct the biases in the GCMs. Next, the future changes were investigated by analyzing the multi-model ensemble (MME) of the bias-corrected dataset during 2025–2044 (near-term), 2045–2064 (mid-term), and 2081–2100 (long-term) periods, with reference to the baseline period 1995–2014, under three integrated scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) of the Shared Socioeconomic Pathways (SSPs) and the Representative Concentration Pathways (RCPs). Results show that: (1) the biases in CMIP6 GCMs can be effectively corrected by EQM, and the MME performs better than individual models for each climatic variable. (2) Precipitation over the YRB from 2025 to 2100 is projected to increase at the rate of 9.66 mm/decade, 13.45 mm/decade, and 21.01 mm/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, the annual precipitation is projected to increase by 10.41%, 10.66% and 15.80%, under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. (3) T max (T min) over the YRB is projected to increase by 0.09 (0.07) °C/decade, 0.29 (0.27) °C/decade, and 0.66 (0.64) °C/decade under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, respectively. In the long-term, T max (T min) averaged over the YRB is projected to increase by 1.75 (1.50) °C, 2.72 (2.54) °C and 5.04 (4.85) °C under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5, respectively. (4) Uncertainties in the projected precipitation and temperature over the YRB were reduced based on MME. But further researches, such as selecting the superior models with respect to the regional climate of the YRB from the CMIP6 and using the ensemble methods that assign weight based on the performance of each model, are still needed to provide more reliable climate projections. • Projected changes were explored by 23 CMIP6 GCMs under the three SSP-RCP scenarios. • Biases in the GCMs were corrected by Empirical Quantile Mapping (EQM) method. • Multi-model ensemble (MME) was used to reduce the uncertainties. [ABSTRACT FROM AUTHOR]