Extreme precipitation often persists for multiple days with variable duration but has usually been examined at fixed duration. Here we show that considering extreme persistent precipitation by complete event with variable duration, rather than a fixed temporal period, is a necessary metric to account for the complexity of changing precipitation. Observed global mean annual‐maximum precipitation is significantly stronger (49.5%) for persistent extremes than daily extremes. However, both globally observed and modeled rates of relative increases are lower for persistent extremes compared to daily extremes, especially for Southern Hemisphere and large regions in the 0‐45°N latitude band. Climate models also show significant differences in the magnitude and partly even the sign of local mean changes between daily and persistent extremes in global warming projections. Changes in extreme precipitation therefore are more complex than previously reported, and extreme precipitation events with varying duration should be taken into account for future climate change assessments.

Extreme climate events have inflicted severe and adverse effects on human life, social economy, and natural ecosystems. In this study, the precipitation time series from a network of 90 weather stations in Northeast China (NEC) and for the period of 1961–2009 are used. An objective method, the multifractal detrended fluctuation analysis method, is applied to determine the thresholds of extreme events. Notable occurrence frequency and strong intensity of extreme precipitation (EP) mainly occur in Liaoning Province and the piedmont regions in Changbai Mountains and Xiao Hinggan Mountains. Generally, EP frequency shows a nonsignificant negative trend, whereas EP intensity has a weak and nonsignificant positive trend for the entire NEC in the period of 1961–2009. To assess EP severity, we propose an EP severity index (EPSI) combining both EP frequency and intensity, rather than separately analyze the EP frequency or intensity. Spatial gradients of EPSI are observed in northeast‐southwest and northwest‐southeast directions over NEC. The EPSI in northwestern and southeastern NEC are low (0.02–0.3), whereas high EPSI (0.34–0.83) occurs in the southwestern and northeastern portions of the region. Higher EPSI (0.4–0.83) occurs in southern Liaoning Province, which decreases along the southwest‐northeast direction. The spatial patterns of EPSI are associated with the circulation over East Asia. Areas that have a short distance from sea and that locate in the windward slope of mountain will probably accompany high EP severity over NEC.

Climate involves different combinations of temperature and precipitation, and each year's combination of factors can be assigned a climatic year type (CYT; e.g. Warm‐Humid). Describing the changes in the CYT provides more information than describing the temperature or precipitation data alone. In this study, we defined nine CYTs using the probability density function of annual temperature and precipitation. Recent and future spatiotemporal changes in CYT were analysed using 507‐station observational data and projected data obtained from the CMIP5 multi‐model ensemble under RCP2.6, RCP4.5, and RCP8.5 scenarios. China was divided into six subregions to analyse the spatiotemporal changes. Obvious differences in spatial patterns among the various CYTs reflect the climate regime throughout China. The warmth‐associated CYTs (Warm‐Humid, Warm‐Dry, and Warm‐Normal) mainly occur in West China (e.g. Southwest China). The cold‐associated CYTs (Cold‐Humid, Cold‐Dry, and Cold‐Normal) dominate at high latitudes and high altitudes (e.g. Northeast China and the Tibetan Plateau). The climate in China changed from cold to warm in the last half‐century, accompanying the transformation of Cold‐Humid, Cold‐Dry, and Cold‐Normal before the early 1990s to Warm‐Humid, Warm‐Dry, and Warm‐Normal from the early 1990s onward. In the 21^st century, the projected CYTs are mainly Warm‐Humid, Warm‐Dry, and Warm‐Normal in China. Warm‐Humid dominates in West China, North China, and Northeast China. Warm‐Dry is mainly projected in the Yellow River Valley and South China. High‐frequency Warm‐Normal is projected in the Yellow River Valley. Warm‐Humid is projected to increase whereas Warm‐Dry and Warm‐Normal are projected to decrease from 2015 to 2099. All three CYTs are projected to exhibit larger changes in trends under stronger versus weaker RCPs (RCP8.5 > RCP4.5 > RCP2.6). Compared with temperature or precipitation data alone, CYTs provide more complete information on climate change and more accurately characterize regional differences in climate throughout China.

Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics.

Changes in extreme precipitation differ among regions (e.g., dry and wet regions), causing different adverse effects on human life, social economy, and natural ecosystems on a warming planet. In this study, we use the 95th percentile‐based threshold to define extreme precipitation. The spatiotemporal changes in extreme precipitation and temperature‐extreme precipitation scaling relationship between dry and wet regions of mainland China during 1961‐2014 are analyzed using two gridded data sets of precipitation and temperature observations. Extreme precipitation increased faster in dry regions (3.9% frequency and 2.8% relative intensity per decade) compared to wet regions (2.5% frequency and 2.1% relative intensity per decade) over the past 54 years. Despite the negative regression relationship between mean precipitation and temperature in wet regions, extreme precipitation has a positive relationship with temperature. The regression coefficient in relative intensity against temperature change is 6% per °C in wet regions. The frequency increases significantly (p < 0.01) faster as temperature increases in wet regions (11.1% per °C) compared with dry regions (6.9% per °C). These results suggest that precipitation extremes increase for both dry and wet regions, and faster trends are observed in dry regions. However, the same magnitude of warming causes more extreme precipitation in wet regions. Therefore, more extreme precipitation in future may raise the risk of flooding in both dry and wet regions, particularly for wet regions, due to the stronger relative intensity and faster increases against warming.