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The overarching goal of this paper is to shed light on the human influence on the changing patterns of heat waves in India using the Heat Wave Magnitude Index daily (HWMId). The HWMId obtained from the observational data sets shows a large increase in the heat waves during the past decades. Investigating the effects of natural (e.g., solar variations and volcanic forcings) and anthropogenic (e.g., greenhouse gas emissions, anthropogenic, land use, and land cover) forcings revealed that the anthropogenic factors have cause a two-fold increase in the occurrence probability of severe heat waves in central and mid-southern India during twentieth century. The spatial distribution of maximum HWMId values under natural and all forcings (including anthropogenic) indicates that in most places human activities have increases the frequency, duration and intensity of extreme heat waves. Under the Representative Concentration Pathway (RCP) 4.5, the risk of heat waves is projected to increase tenfold during the twenty-first century. More than ~ 70% of the land areas in India is projected to be influenced by heat waves with magnitudes greater than 9. Furthermore, we find a significant relationship between heat waves and deficits in precipitation. Results show that concurrent heat waves and droughts are projected to increase in most places in India during the twenty-first century.

In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge-based, satellite-related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high-latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation.

Understanding the impact of different external forcings on regional temperature changes in China and constraining future changes is important for formulating targeted climate mitigation policies. We conducted a detection and attribution analysis to examine the effects of different external forcings on the differences in warming across China and seven subregions. This analysis spanned from 1965 to 2014 and used observations as well as data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The signals of anthropogenic (ANT) forcing can be detected and separated from natural (NAT) forcing in all regions, and the effect of greenhouse gases are found to account for a significant portion of the observed warming in these regions. In the Yangtze River Basin, southeast China and southwest China, the observed warming trends are lower than other subregions. This can be attributed to the relatively weak impact of greenhouse gases, resulting in a temperature increase of only 0.51°C in the Yangtze River Basin, which accounts for 48.57% of the observed warming. While both southeast China and southwest China experience a cooling effect of aerosols, leading to temperature decreases of 0.20 and 0.53°C, respectively, even though the signal of aerosol forcing is not detected. In terms of future projection, the use of attribution constraint approach effectively corrects the biases of the CMIP6 model simulation and reduces projection uncertainty in China and most subregions under the new shared socioeconomic pathway (SSP) SSP2‐4.5 and SSP5‐8.5. The Tibetan Plateau has the largest reduction of the uncertainty range among seven subregions, with the decrease of 39.02% under SSP2‐4.5 scenario in the long‐term (2081–2098). In addition, north China and southwest China will experience the highest and lowest warming in the long‐term (2081–2098) after attribution constraints, increasing by 3.41 and 2.14°C, respectively, under SSP2‐4.5 scenario. [ABSTRACT FROM AUTHOR]

Both the 2015/16 strong El Nino and anthropogenic factors contributed to the June-July 2016 extreme precipitation (R20mm, RX5day) in Yangtze-Huai, China. Combined, they increased the risk of the event tenfold. [...]

This study provides a comprehensive analysis of the human contribution to the observed intensification of precipitation extremes at different spatial scales. We consider the annual maxima of the logarithm of 1-day (Rx1day) and 5-day (Rx5day) precipitation amounts for 1950–2014 over the global land area, four continents, and several regions, and compare observed changes with expected responses to external forcings as simulated by CanESM2 in a large-ensemble experiment and by multiple models from phase 6 of the Coupled Model Intercomparison Project (CMIP6). We use a novel detection and attribution analysis method that is applied directly to station data in the areas considered without prior processing such as gridding, spatial or temporal dimension reduction, or transformation to unitless indices and uses climate models only to obtain estimates of the space–time pattern of extreme precipitation response to external forcing. The influence of anthropogenic forcings on extreme precipitation is detected over the global land area, three continental regions (the western Northern Hemisphere, western Eurasia, and eastern Eurasia), and many smaller IPCC regions, including central North America, East Asia, east-central Asia, eastern Europe, eastern North America, northern Europe, and western Siberia for Rx1day, and central North America, eastern Europe, eastern North America, northern Europe, the Russian Arctic region, and western Siberia for Rx5day. Consistent results are obtained using forcing response estimates from either CanESM2 or CMIP6. Anthropogenic influence is estimated to have substantially decreased the approximate waiting time between extreme annual maximum events in regions where anthropogenic influence has been detected, which has important implications for infrastructure design and climate change adaptation policy. Significance Statement: All previous detection and attribution studies of observed changes in extreme precipitation (i) use station data that has been heavily processed via gridding, transformation, and spatial and temporal averaging or other dimension reduction approaches, as well as using climate models to estimate the responses to external forcing, and (ii) also use models to estimate the unforced natural variability of extreme precipitation. Both aspects reduce user confidence in detection and attribution results. This study uses station data directly and avoids difficult to verify model-based estimates of the unforced variability of precipitation extremes. Results confirm findings from previous studies, and extend them to a number of subcontinental regions, thus substantially increasing confidence in detection and attribution findings precipitation. [ABSTRACT FROM AUTHOR]

This study evaluates the 14 global climate models in the Coupled Model Intercomparison Project Phase 5 in capturing the extreme climate events and the agricultural climate indices over China. Future climate event changes in different Representative Concentration Pathway scenarios in China are projected using the Reliability Ensemble Average method. The models can simulate the basic spatial distribution characteristics of indices and simulate temperature indices better than precipitation indices. The models overestimate the number of wet days over China, which is related to the tendency that rainfall is simulated too often and too lightly in models. The underestimation of the number of very heavy precipitation events is detected in most models, while overestimation occurred in northern China and the Qinghai-Tibet Plateau region. Some factors, such as the complex topography, the moist convection schemes in models, and the ability to simulate large-scale circulation, may result in the performances for precipitation. Higher active accumulated temperature and more summer days are projected, while the accumulated negative temperature and frost days are projected to decrease with the warming climate. Northern China would become wetter when the number of wet days (R1mm) increases. Extreme wet events are projected to be more probable in South and Central China and the Southwest Region, with increases in very heavy precipitation days (R20mm) in all scenarios. These changes in extreme climate events and agricultural climate indices would inevitably affect agriculture in China. [ABSTRACT FROM AUTHOR]

ABSTRACT Present and future climate change information is required to develop adaptation and mitigation strategies at national and international levels. This study assessed the simulated surface air temperature ( SAT) and precipitation ( PR) over China from 24 models involved in the Coupled Model Intercomparison Project Phase 5 ( CMIP5). The reliability ensemble average ( REA) is applied to project the SAT and PR change under representative concentration pathway ( RCP) scenarios over China in the 21st century. The results show that most CMIP5 models tend to underestimate SAT and overestimate PR in China. Models generally agree better with the observed SAT than PR. For SAT, the ensemble prediction shows that warming is expected all over China for all RCPs. The warming trend from 2006 to 2099 in China is 0.87 ± 0.14 °C 100 year−1, 2.47 ± 0.48 °C 100 year−1, 5.85 ± 0.73 °C 100 year−1 for RCP 2.6, RCP 4.5 and RCP 8.5, respectively. Northern regions experience more warming than southern regions. The Songhua River basin warms the most, considering the ten studied basins for RCP 4.5 and RCP 8.5. Under RCP 2.6, the largest warming trend occurs in the Huaihe River basin. For PR, the spatial pattern of PR change has zonal characteristics. The girds with the maximum linear trend, i.e. >7.5 mm decade−1, are concentrated in the upper Yangtze River basin. For temporal scale, PR in China is also projected to increase during the 21st century by 4.89 ± 2.30% 100 year−1, 8.67 ± 6.27% 100 year−1 and 13.39 ± 12.58% 100 year−1 for RCP 2.6, RCP 4.5 and RCP 8.5, respectively. PR tends to decrease in the Yangtze River basin, Southeast River Drainage and Pearl River basin during the early period (2011-2030) for all RCPs, largely increase thereafter. However, uncertainties are unavoidable for SAT and PR projections. The PR uncertainty exceeds the temperature uncertainty. More studies regarding the analysis of narrowing uncertainties are essential for a better understanding of climate change. [ABSTRACT FROM AUTHOR]