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In recent years, the frequent occurrence of extreme climate disasters has seriously affected agriculture in Northeast China. Based on precipitation data derived from 83 geographical stations in the study area for 60 years (1960–2019), we chose the reliable statistical methods of the Mann–Kendall test, Sen's slope, and the Standardized Precipitation Index, and regarded drought and flood as a whole in this paper, to identify the temporal and spatial variation characteristics of precipitation and extreme climate events in Northeast China. The results show that an increasing trend of disasters was detected at the 95% confidence level (Z = 2.3). According to the timescale analysis, abrupt climate changes started in 2006. Temporal and spatial distribution of extreme climate disasters, mainly drought and flood disasters, showed a significant upward trend from 2006 to 2019. According to the spatial analysis, the precipitation in Northeast China decreased from south to north and fluctuated less from east to west. Moreover, stations with extreme climate trends (trend of climatic anomaly with confidence level > 90%) followed the same spatial pattern as those with a high frequency of extreme climate disasters (more than 17.87 times/decade). The severity and frequency of extreme climate have increasingly threatened Northeast China in the past decade. In particular, the Northeast Plain experienced the most severe and extreme climate events that seriously threatened the study area in 2007, 2009, 2010, and 2019. Our results highlight the urgent need for the development of monitoring and early warning of droughts and flood disasters to reduce economic losses. [ABSTRACT FROM AUTHOR]

Extreme climate events frequently exert serious effects on terrestrial vegetation activity. However, these effects are still uncertain in widely distributed areas with different climate zones. Transect analysis is important to understand how terrestrial vegetation responds to climate change, especially extreme climate events, by substituting space for time. In this paper, seven extreme climate indices and the Normalized Difference Vegetation Index (NDVI) are employed to examine changes in the extreme climate events and vegetation activity. To reduce the uncertainty of the NDVI, two satellite-derived NDVI datasets, including the third generation Global Inventory Monitoring and Modeling System (GIMMS-3g) NDVI dataset and the NDVI from the National Oceanic and Atmospheric Administration (NOAA) satellites on Star Web Servers (SWS), were employed to capture changes in vegetation activity. The impacts of climate extremes on vegetation activity were then assessed over the period of 1982–2012 using the North–South Transect of Eastern China (NSTEC) as a case. The results show that vegetation activity was overall strengthened from 1982 to 2012 in the NSTEC. In addition, extreme high temperature events revealed an increased trend of approximately 5.15 days per decade, while a weakened trend (not significant) was found in extreme cold temperature events. The strengthened vegetation activities could be associated with enhanced extreme high temperature events and weakened extreme cold temperature events over the past decades in most of the NSTEC. Despite this, inversed changes were also found locally between vegetation activity and extreme climate events (e.g., in the Northeast Plain). These phenomena could be associated with differences in vegetation type, human activity, as well as the combined effects of the frequency and intensity of extreme climate events. This study highlights the importance of accounting for the vital roles of extreme climate effects on vegetation activity. [ABSTRACT FROM AUTHOR]

This study examines potential benefits of performance‐based multi‐model ensembles (MMEs) in projecting the impacts of climate change on extreme precipitation indices over East Asia (EA) using the data from 19 GCMs in the coupled model intercomparison project 5 (CMIP5). The Taylor skill score is adopted as the measure of the model skills in simulating the spatial and interannual variability of the selected extreme precipitation indices over four EA regions. The overall rank based on the total skill score (TSC) is used to construct two skill‐based MMEs, MME of high‐skill, MMH (MME of low‐skill, MML) that include the top (bottom) seven models, in addition to the simple ensemble of all 19 GCMs (ENS). Inter‐GCM consistency is measured using the signal‐to‐noise ratio (SNR). In the present‐day period, MMH yields higher skill scores than MML and ENS for almost all extreme precipitation indices as well as regions. Regional variations in biases, inter‐model consistency, and TSC are large. The inter‐model consistency is highest for Northern China and Manchuria and is lowest for Southern China. The most notable differences in the key properties of climate change signals from the three MMEs among the three ensembles are that the climate change signals from MMH and ENS exceed the 90% significance level in much larger areas than those from MML. However, the differences in the climate change signals between MMH and MML are generally below the 90% significance level. The SNR of the projected climate change signals shows that MMH yields more consistent climate change signals than ENS/MML. Both the SNR differences and the area in which statistical significance exceed the 90% level suggest that constructing climate change signals from a group of higher‐skill models may yield more reliable projections than constructing MMEs from the entire models or a group of lower‐skilled models. The domains used in the study (SOUTH: 110°–140°E, 20°–30°N, MIDDLE: 110°–140°E, 30°–40°N), NORTH: 110°–140°E, 40°–50°N, EA: 110°–140°E, 20°–50°N) [ABSTRACT FROM AUTHOR]