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Seasonal prediction of extreme precipitation has long been a challenge especially for the East Asian Summer Monsoon region, where extreme rains are often disastrous for the human society and economy. This paper introduces a decision‐tree (DT) method for predicting extreme precipitation in the rainy season over South China in April–June (SC‐AMJ) and the North China Plain in July–August (NCP‐JA). A number of preceding climate indices are adopted as predictors. In both cases, the DT models involving ENSO and NAO indices exhibit the best performance with significant skills among those with other combinations of predictors and are superior to their linear counterpart, the binary logistic regression model. The physical mechanisms for the DT results are demonstrated by composite analyses of the same DT path samples. For SC‐AMJ, an extreme season can be determined mainly via two paths: the first follows a persistent negative NAO phase in February–March; the second goes with decaying El Niño. For NCP‐JA, an extreme season can also be traced via two paths: the first is featured by "non El Niño" and an extremely negative NAO phase in the preceding winter; the second follows a shift from El Niño in the preceding winter to La Niña in the early summer. Most of the mechanisms underlying the decision rules have been documented in previous studies, while some need further studies. The present results suggest that the decision‐tree approach takes advantage of discovering and incorporating various nonlinear relationships in the climate system, hence is of great potential for improving the prediction of seasonal extreme precipitation for given regions with increasing sample observations. [ABSTRACT FROM AUTHOR]

Numerous circulation indices have been applied in practical climate services focused on regional precipitation. It is beneficial to identify the most influential or decisive indices, but this is difficult with conventional correlation analyses because of the underlying nonlinear mechanisms for precipitation. This paper demonstrates a set of the most influential indices for July–August precipitation in North China, based on the recursive random forest (RRF) method. These decisive circulation indices include the Polar–Eurasia teleconnection, North African subtropical high ridge position, India–Burma trough, Antarctic Oscillation, Northern Hemisphere polar vortex central latitude, North Atlantic Oscillation, and western Pacific subtropical high northern boundary position. Some of these factors have been recognized as directly influential to the regional precipitation, for example, those of the northwestern Pacific subtropical high; however, some are not easily understood. Decision tree (DT) models using these indices were developed to facilitate composite analyses to explain the RRF results. Taking one of the most interesting DT rules as an example, when the North African subtropical high ridge position is sufficiently far south, an anomalous anticyclone occurs in the upstream and an anomalous cyclone in the downstream of North China. This is unfavorable for northward moisture transport in eastern China and hence causes less precipitation in North China than climatology. The present results are not only helpful for improving diagnostic models of regional precipitation, but also enlightening for exploring how global climate change could impact a region by modulating large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

The potential predictability of seasonal extreme precipitation accumulation (SEPA) across mainland China is evaluated, based on daily precipitation observations during 1960-2013 at 675 stations. The potential predictability value (PPV) of SEPA is calculated for each station by decomposing the observed SEPA variance into a part associated with stochastic daily rainfall variability and another part associated with longer-time-scale climate processes. A Markov chain model is constructed for each station and a Monte Carlo simulation is applied to estimate the stochastic part of the variance. The results suggest that there are more potentially predictable regions for summer than for the other seasons, especially over southern China, the Yangtze River valley, the north China plain, and northwestern China. There are also regions of large PPVs in southern China for autumn and winter and in northwestern China for spring. The SEPA series for the regions of large PPVs are deemed not entirely stochastic, either with long-term trends (e.g., increasing trends in inland northwestern China) or significant correlation with well-known large-scale climate processes (e.g., East Asian winter monsoon for southern China in winter and El Niño for the Yangtze River valley in summer). This fact not only verifies the claim that the regions have potential predictability but also facilitates predictive studies of the regional extreme precipitation associated with large-scale climate processes. [ABSTRACT FROM AUTHOR]

ABSTRACT Two circulation index-based methods were applied to downscale summer daily rainfall at four meteorological stations in Chongqing, China. One is a regression method ( CIM) and the other a hybrid circulation classification plus regression method ( CPM). Both methods used the same three circulation indices and surface specific humidity as predictors. In the first method ( CIM), the indices and humidity were used directly as predictors and only one model was developed at each station. In the second method ( CPM), the indices were further used to define circulation patterns using the objective Lamb-Jenkinson classification scheme, and one model was built using humidity as the sole predictor for each pattern. Logistic regression was used to define rainfall probability, and a gamma distribution was fit using observations to randomly generate daily rainfall amounts. The two downscaling methods were validated and compared. The results suggest that (1) both methods yield consistently reasonable results with respect to occurrence and amount of daily rainfall, although they perform poorly in reproducing interannual and interdecadal variability, (2) specific humidity should be used as a predictor. Both methods were forced by two future scenarios of a global climate model ( GCM) to demonstrate the added value with two methods. The downscaled scenarios show a shift towards larger rainfall values, accompanied by more frequent dry days. This shift is mainly attributed to a change in specific humidity. Despite the similar performances of the two methods, CPM gives a higher frequency of dry days than CIM, whereas CIM produces stronger intensity of rainfall than CPM. This provides additional information about the uncertainty in the projections. [ABSTRACT FROM AUTHOR]

Daily precipitation series at 15 stations in the Beijing metropolitan region (BMR) during 1960-2012 were homogenized using the multiple analysis of series for homogenization method, with additional adjustments based on analysis of empirical cumulative density function (ECDF) regarding climate extremes. The cumulative density functions of daily precipitation series, the trends of annual and seasonal precipitation, and summer extreme events during 1960-2012 in the original and final adjusted series at Beijing station were comparatively analyzed to show the necessity and efficiency of the new method. Results indicate that the ECDF adjustments can improve the homogeneity of high-order moments of daily series and the estimation of climate trends in extremes. The linear trends of the regional-mean annual and seasonal (spring, summer, autumn, and winter) precipitation series are −10.16, 4.97, −20.04, 5.02, and −0.11 mm (10 yr), respectively. The trends over the BMR increase consistently for spring/autumn and decrease for the whole year/summer; however, the trends for winter decrease in southern parts and increase in northern parts. Urbanization affects local trends of precipitation amount, frequency, and intensity and their geographical patterns. For the urban-influenced sites, urbanization tends to slow down the magnitude of decrease in the precipitation and extreme amount series by approximately −10.4% and −6.0%, respectively; enhance the magnitude of decrease in precipitation frequency series by approximately 5.7%; reduce that of extremes by approximately −8.9%; and promote the decreasing trends in the summer intensity series of both precipitation and extremes by approximately 6.8% and 51.5%, respectively. [ABSTRACT FROM AUTHOR]

Daily precipitation and temperature records at 13 stations for the period 1960-2008 were analyzed to identify climatic change and possible effects of urbanization on low-temperature precipitation [LTP, precipitation of ⩾ 0.1 mm d occurring under a daily minimum temperature (Tmin) of ⩽ 0°C] in the greater Beijing region (BJR), where a rapid process of urbanization has taken place over the last few decades. The paper provides a climatological overview of LTP in BJR. LTP contributes 61.7% to the total amount of precipitation in BJR in the cold season (November-March). There is a slight increasing trend [1.22 mm (10 yr)] in the amount of total precipitation for the cold season during 1960-2008. In contrast, the amount of LTP decreases by 0.6 mm (10 yr). The warming rate of Tmin in BJR is 0.66°C (10 yr). Correspondingly, the frequency of LTP decreases with increasing Tmin by −0.67 times per °C. The seasonal frequency and amount of LTP in southeast BJR (mostly urban sites) are 17%-20% less than those in the northwestern (rural and montane sites). The intensity of LTP for the urban sites and northeastern BJR exhibited significant enhancing trends [0.18 and 0.15 mm d (10 yr), respectively]. The frequency of slight LTP (<0.2 mm d) significantly decreased throughout BJR [by about 5.74% (10 yr) in the urban area and northeast BJR], while the contribution of the two heaviest LTP events to total LTP amount significantly increased by 3.2% (10 yr). [ABSTRACT FROM AUTHOR]

Global warming exerts a lengthening effect on the growing season, with observational evidences emerging from different regions over the world. However, the difficulty for a global overview of this effect for the last century arises from limited availability of the long-term daily observations. In this study, we find a good linear relationship between the start (end) date of local growing season (LGS) and the monthly mean temperature in April (October) using the global gridded daily temperature dataset for 1960-1999. Using homogenized daily temperature records from nine stations where the time series go back to the beginning of the twentieth century, we find that the rate of change in the start (end) date of the LGS for per degree warming in April (October) mean temperature keeps nearly constant throughout the time. This enables us to study LGS changes during the last century using global gridded monthly mean temperature data. The results show that during the period 1901-2009, averaged over the observation areas, the LGS length has increased by a rate of 0.89 days decade, mainly due to an earlier start (−0.58 days decade). This is smaller than those estimates for the late half of the twentieth century, because of multidecadal climate variability (MDV). A MDV component of the LGS index series is extracted by using Ensemble Empirical Mode Decomposition method. The MDV exhibits significant positive correlation with the Atlantic Multi-decadal Oscillation (AMO) over most of the Northern Hemisphere lands, but negative in parts of North America and Western Asia for start date of LGS. These are explained by analyzing differences in atmospheric circulation expressed by sea level pressure departures between the warm and cool phases of AMO. It is suggested that MDV in association with AMO accelerates the lengthening of LGS in Northern Hemisphere by 53 % for the period 1980-2009. [ABSTRACT FROM AUTHOR]

Climate extremes and changes in eastern China are closely related to variations of the East Asian summer monsoon and corresponding atmospheric circulations. The relationship between frequencies of temperature and precipitation extremes in China during the last half century is investigated using Singular Value Decomposition analysis. During 1980-1996, there was a typical pattern with fewer hot days and more precipitation extremes in the northern part of eastern China, and more hot days and fewer precipitation extremes in the southern part. This geographic pattern tended to reverse after 1997, with fewer hot days and more extreme precipitation days south of the Yangtze River and vice versa to the north. Differences in atmospheric circulation between the former and latter periods are presented. We conclude that a mid-level anomalous high/low, upper-level anomalous easterlies/westerlies over the north/south of eastern China, a weakened East Asian summer monsoon and associated upper-tropospheric center of cooling (30°N, 110°E) are all favorable for the changes in frequencies of temperature and precipitation extremes. [ABSTRACT FROM AUTHOR]

Changes of the frequency of precipitation extremes (the number of days with daily precipitation exceeding the 90th percentile of a daily climatology, referred to as R90N) in summer (June-August) over the mid-lower reaches of the Yangtze River are analyzed based on daily observations during 1961-2007. The first singular value decomposition (SVD) mode of R90N is linked to an ENSO-like mode of the sea surface temperature anomalies (SSTA) in the previous winter. Responses of different grades of precipitation events to the climatic mode are compared. It is notable that the frequency of summer precipitation extremes is significantly related with the SSTA in the Pacific, while those of light and moderate precipitation are not. It is suggested that the previously well-recognized impact of ENSO on summer rainfall along the Yangtze River is essentially due to a response in summer precipitation extremes in the region, in association with the East Asia-Pacific (EAP) teleconnection pattern. A negative relationship is found between the East Asian Summer Monsoon (EASM) and precipitation extremes over the mid-lower reaches of the Yangtze River. In contrast, light rainfall processes are independent from the SST and EASM variations. [ABSTRACT FROM AUTHOR]

The prediction of summer precipitation patterns (PPs) over eastern China is an important and topical issue in China. Predictors that are selected based on historical information may not be suitable for the future due to non-stationary relationships between summer precipitations and corresponding predictors, and might induce the instability of prediction models, especially in cases with few predictors. This study aims to investigate how to learn as much information as possible from various and numerous predictors reflecting different climate conditions. An objective prediction method based on the multinomial logistic regression (MLR) model is proposed to facilitate the study. The predictors are objectively selected from a machine learning perspective. The effectiveness of the objective prediction model is assessed by considering the influence of collinearity and number of predictors. The prediction accuracy is found to be comparable to traditionally estimated predictability, ranging between 0.6 and 0.7. The objective prediction model is capable of learning the intrinsic structure of the predictors, and is significantly superior to the prediction model with randomly-selected predictors and the single best predictor. A robust prediction can be generally obtained by learning information from plenty of predictors, although the most effective model may be constructed with fewer predictors through proper methods of predictor selection. In addition, the effectiveness of objective prediction is found to generally improve as observation increases, highlighting its potential for improvement during application as time passes. [ABSTRACT FROM AUTHOR]