Compared to individual hot days/nights, compound hot extremes that combine daytime and nighttime heat are more impactful. However, past and future changes in compound hot extremes as well as their underlying drivers and societal impacts remain poorly understood. Here we show that during 1960-2012, significant increases in Northern Hemisphere average frequency (~1.03 days decade −1 ) and intensity (~0.28°C decade −1 ) of summertime compound hot extremes arise primarily from summer-mean warming. The forcing of rising greenhouse gases (GHGs) is robustly detected and largely accounts for observed trends. Observationally-constrained projections suggest an approximate eightfold increase in hemispheric-average frequency and a threefold growth in intensity of summertime compound hot extremes by 2100 (relative to 2012), given uncurbed GHG emissions. Accordingly, endof-century population exposure to compound hot extremes is projected to be four to eight times the 2010s level, dependent on demographic and climate scenarios.

The southeast region of China is frequently affected by summer heat waves. Nanjing, a metropolitan city in Jiangsu Province, China, experienced an extreme 14-day heat wave in 2013. Extreme heat can not only induce health outcomes in terms of excess mortality and morbidity (hospital admissions) but can also cause productivity losses for self-paced indoor workers and capacity losses for outdoor workers due to occupational safety requirements. All of these effects can be translated into productive working time losses, thus creating a need to investigate the macroeconomic implications of heat waves on production supply chains. Indeed, industrial interdependencies are important for capturing the cascading effects of initial changes in factor inputs in a single sector on the remaining sectors and the economy. To consider these effects, this paper develops an interdisciplinary approach by combining meteorological, epidemiological and economic analyses to investigate the macroeconomic impacts of heat waves on the economy of Nanjing in 2013. By adopting a supply-driven input-output (IO) model, labour is perceived to be a key factor input, and any heat effect on human beings can be viewed as a degradation of productive time and human capital. Using this interdisciplinary tool, our study shows a total economic loss of 27.49 billion Yuan for Nanjing in 2013 due to the heat wave, which is equivalent to 3.43% of the city's gross value of production in 2013. The manufacturing sector sustained 63.1% of the total economic loss at 17.34 billion Yuan. Indeed, based on the ability of the IO model to capture indirect economic loss, our results further suggest that although the productive time losses in the manufacturing and service sectors have lower magnitudes than those in the agricultural and mining sectors, they can entail substantial indirect losses because of industrial interdependencies. This important conclusion highlights the importance of incorporating industrial interdependencies and indirect economic assessments in disaster risk studies

Abstract. This paper evaluates the performance of a statistical post-processor for imperfect hydrologic model forecasts. Assuming that the meteorological forecasts are wellcalibrated, we employ a "General Linear Model (GLM)" to post-process simulations produced by a hydrologic model. For a particular forecast date, the observations and simulations from an "analysis window" and hydrologic model forecasts for a "forecast window", the GLM Post-Processor (GLMPP) is used to produce an ensemble of predictions of the streamflow observations that will occur during the "forecast window". The objectives of the GLMPP are to: (1) preserve any skill in the original hydrologic ensemble forecast; (2) correct systematic model biases; (3) retain the equallikelihood assumption for the ensemble; (4) preserve temporal scale dependency relationships in streamflow hydrographs and the uncertainty in the predictions; and, (5) produce reliable ensemble predictions.Observed and simulated daily streamflow data from the Second Workshop on Model Parameter Estimation Experiment (MOPEX) are used to test how well these objectives are met when the GLMPP is applied to ensemble hydrologic forecasts driven by well calibrated meteorological forecasts. A 39-year hydrologic dataset from the French Broad basin is split into calibration and verification periods. The results show that the GLMPP built using data from the calibration period removes the mean bias when applied to hydrologic model simulations from both the calibration and verification periods. Probability distributions of the post-processed model simulations are shown to be closer to the climatological probability distributions of observed streamflow than the distributions of the unadjusted simulated flows. A number of Correspondence to: Q. Duan (qyduan@bnu.edu.cn) experiments with different GLMPP configurations were also conducted to examine the effects of different configurations for forecast and analysis window lengths on the robustness of the results.