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Long‐range U.S. summer rainfall prediction skill is low. Monsoon variability, especially over the West North Pacific Monsoon (WNPM) and/or East Asian Monsoon (EAM) region, can influence U.S. Great Plains hydroclimate variability via a forced Rossby wave response. Here, we explored subseasonal monsoon variability as a source of predictability for Great Plains rainfall. The boreal summer intraseasonal oscillation (BSISO) is related to Great Plains convection and Great Plains low‐level jet (LLJ) anomalies as well as a cross‐Pacific wave train. Using a causal effect network, we found that the time between BSISO‐related geopotential height anomalies and Great Plains rainfall anomalies is about 2 weeks; therefore, BSISO convection may be a valuable forecast of opportunity for subseasonal prediction of Great Plains convection anomalies. More specifically, causal link patterns/maps revealed that the above‐normal weekly EAM rainfall, rather than WNPM rainfall or general geopotential height activity over the East Asia, was causally linked to Great Plains LLJ strengthening and active Great Plains convection the following week. Plain Language Summary: Forecasting U.S. summer rainfall out past ∼7–10 days, considered the subseasonal timescale, is a challenge. However, there are times when subseasonal forecast skill is relatively higher due to propagating atmospheric waves from remote climate events. The West North Pacific Monsoon (WNPM) and East Asian Monsoon (EAM) can provide a remote influence via their related heating triggering atmospheric waves. Using an algorithm that effectively identifies and quantifies cause‐and‐effect relationships, we constructed a network that connects EAM rainfall and Great Plains rainfall via these atmospheric waves. We found that above‐normal EAM rainfall, rather than WNPM rainfall or general atmospheric wave activity over the East Asia, was causally linked to active Great Plains convection the following week. Therefore, monsoon variability, especially over the EAM region, might be a valuable source of subseasonal predictability for U.S. summer rainfall. Key Points: Subseasonal monsoon variability is linked to rainfall signals over U.S. Great Plains and its associated dynamical driversA cause‐and‐effect algorithm verified a pathway from regional monsoon rainfall to Great Plains rainfall, which takes approximately 2 weeksWeekly East Asian monsoon rainfall is causally linked to Rossby wave excitation and active Great Plains convection about 1 week later [ABSTRACT FROM AUTHOR]

Using hindcast and forecast data from advanced prediction systems of NCEP CFSv2 and JMA/MRI CPSv2 for the winter 1982/1983–2017/2018, this study investigates the predictability of the climatology and dominant modes of winter-mean surface air temperature (SAT) over East Asia. Although the simulated climatological mean SAT has a large bias over most of the mainland China, both models show high forecasting skills for the principal components (PC1 and PC2) of the two major modes (northern and southern temperature modes) of the winter-mean SAT over East Asia 1 month in advance. In comparison, the MRI-CPSv2 performs better than the CFSv2 in simulating the spatial distribution of the northern temperature mode and the associated circulation anomalies aloft, but the CFSv2 has better performance than the MRI-CPSv2 in simulating the spatial distribution of southern temperature mode and its linkage to the Arctic Oscillation and Eurasian pattern. Furthermore, both models well simulate the impacts of the dipole sea surface temperature (SST) anomalies over the mid-latitude North Pacific and El Niño–Southern Oscillation on the northern and southern temperature modes, respectively. Accordingly, the forecasting skill and signal-to-noise ratio of the PC1 (PC2) are significantly improved in the years with strong SST anomalies over the mid-latitude North Pacific (tropical central-eastern Pacific), particularly for the CFSv2 (MRI-CPSv2) predictions. These results are beneficial for understanding the interannual predictability of the East Asian winter climate. [ABSTRACT FROM AUTHOR]

Southern China (SC) has been increasingly affected by extreme high-temperature events during summer (July and August, JA) in recent decades. However, to what extent the anomaly pattern of the extreme high temperature days (EHDs) over SC is predictable remains largely unexplored. To address this issue, we build physics-based empirical model to predict the spatial distribution of JA EHDs over SC and examine its predictability. Two major modes of EHDs over SC are identified by empirical orthogonal function analysis based on the observational data during period of 1980–2009. The first mode is a homogeneous EHDs mode related to a prevailing low-level high-pressure anomaly over SC. The second mode of EHDs is a meridional dipole pattern associated with the Pacific-Japan pattern over East Asia/Western Pacific sector and Pacific Meridional Mode-like SSTA over North Pacific. Via tracking the boundary anomalies forcing of EHDs over SC, physically meaningful predictors are selected to establish a set of physics-based empirical (P-E) models for predicting the first two leading principal components (PCs). The two modes can be predicted reasonably well by the P-E models. Thus, they can be regarded as the predictable modes. Assuming that these two modes can be predicted perfectly, 66.1% of the total variance for the distribution of summer EHDs over SC are potentially predictable. Using the first two predicted PCs and corresponding observed spatial pattern of EOF modes, the prediction of anomalies pattern of EHDs over SC can be obtained. The independent forecast skill (2010–2019) of the domain-averaged temporal correlation coefficient is 0.37, which is close to that of 0.44 for maximum potential attainable prediction skill. [ABSTRACT FROM AUTHOR]

Accurate forecasting of the South China Sea summer monsoon (SCSSM) onset is critical for water resource management in the rainy season across East Asia. The present work evaluates the skill of the ECMWF subseasonal‐to‐seasonal (S2S) forecasting system in predicting SCSSM onset dates using a hindcast experiment. The model provides accurate predictions of the SCSSM onset date 10 days in advance. The predictability mainly arises from the sea surface temperature anomaly (SSTA) in the northern Indian Ocean and central‐eastern tropical Pacific, which usually coincides with moderate and extreme decaying El Niño, thus creating windows of opportunity for SCSSM onset forecasting. The model can reasonably reproduce the SCSSM‐related SSTA and its induced atmospheric teleconnections. However, when anomalous Ural blocking influences the mid‐high latitudes, the ECMWF model forecasts of the SCSSM onset are trapped in the synoptic timescale because of the limited ability of the model to consider blocking in forecasting. Plain Language Summary: Accurate predictions of when the monsoon arrives greatly influence agricultural planning. Agro‐based China relies on East Asian summer monsoon rainfall, which allows millions of farmers to feed the most populated country in the world. The summer monsoon over the South China Sea marks the start of the monsoonal rainy season in East Asia. Therefore, forecasting its arrival with a long lead time is essential. A subseasonal forecasting system is an effective tool for monsoon prediction, but little attention has been given to the South China Sea. We use the European Center for Medium‐Range Weather Forecasts system to evaluate subseasonal forecasts of the monsoon onset over the South China Sea. Skillful forecasts can be obtained 10 days in advance. The predictability originates from the accurate representation of the tropical sea surface temperature and its impact on the atmosphere, particularly concomitant with the moderate and extreme decaying El Niño. However, a synoptic system, called Ural blocking, constrains the forecast skill because it is difficult to model. Both the predictive sources and limitations are discussed in our results, which can be used to improve monsoon arrival forecasts. These improvements will benefit the proactive adaptation since policy changes may take several weeks to implement. Key Points: The European Center for Medium‐Range Weather Forecasts S2S model provides accurate 10‐day advance forecasts of the onset date of the South China Sea summer monsoon (SCSSM)The high S2S predictability of the SCSSM onset mainly arises from the forcing of slowly evolving tropical sea surface temperaturesUnpredictable Ural blocking at mid‐high latitudes may hinder forecasts of the SCSSM onset due to synoptic disturbance [ABSTRACT FROM AUTHOR]

Marked by large interannual variability, East Asian summer monsoon (EASM) rainfall has profound socio‐economic impacts through its dominant influence on floods and droughts. Improving predictions of the interannual variations of EASM rainfall has important implications for over 20% of the world's population. While coupled modeling systems have demonstrated some prediction skill related to the El Niño Southern Oscillation with remote influence on EASM rainfall, the impact of soil moisture has heretofore not been systematically investigated. Using a weakly coupled data assimilation (WCDA) system to constrain the soil moisture and soil temperature in a coupled climate model with a global land data assimilation product, this study demonstrates significant improvements in simulating the interannual variations of EASM rainfall, capturing the notable shift to a "wetter‐South‐drier‐North" rainfall pattern in China in the early 1990s. Hindcast simulations initialized with the well‐balanced states from a coupled simulation with WCDA also show significant multi‐year rainfall prediction skill over East China and Tibetan Plateau. Improvements in predicting the EASM rainfall are attributed to the strong land‐atmosphere coupling in large areas over China, which allows improved predictions of soil moisture to influence precipitation through soil moisture‐precipitation feedback, and the effects of land anomalies on the EASM circulation. This study highlights the significant contribution of land to the interannual predictability of EASM rainfall, with a great potential to advance skillful interannual predictions of benefit to the large populations influenced by the annual whiplash of the summer monsoon rain. Plain language summary: The East Asian summer monsoon (EASM) rainfall provides water for over 20% of the world's population but large variability of the monsoon rain can wreak havoc through flood and drought. Previous efforts focusing largely on the El Niño Southern Oscillation to improve interannual prediction of EASM rainfall have provided limited success. By developing a weakly coupled data assimilation system to constrain the soil moisture and soil temperature with observations in a coupled climate model, we demonstrate significant improvements in simulations and hindcasts of EASM rainfall at interannual timescale, attributable to the local and remote impacts of land‐atmosphere interactions. Our results motivate the need to systematically evaluate the contributions of land versus ocean to interannual climate predictability worldwide. Key Points: A weakly coupled data assimilation system is used to constrain soil moisture and soil temperature in coupled climate model experimentsInterannual variations of East Asian summer monsoon rainfall are significantly improved in coupled simulations with data assimilationImprovements are attributed to land‐atmosphere coupling through the influence of soil moisture on precipitation and monsoon circulation [ABSTRACT FROM AUTHOR]

In the winter of 2018/2019, the lower reach of the Yangtze River (LRYR) of China experienced an excessive amount of precipitation with a long duration. Such an extreme event occurred in the mature phase of an El Niño under the background of global warming, and thus, attracted great attention in the society and climate community. Presumably, this extreme event was driven by the El Niño. In this work, based on observational diagnoses and real‐time model forecasts, we investigate the contributions of oceanic forcing and the predictability of this event. It is argued that tropical Atlantic warming, interdecadal variation, and central tropical Pacific warming (associated with Central Pacific [CP] instead of Eastern Pacific El Niño) are three major factors leading to the extreme event. In addition to the recognized impact from sea surface temperature anomalies (SSTAs) associated with CP El Niño, tropical Atlantic warming makes an important contribution to the Atlantic‐Eurasian circulation change through a global zonal wave pattern extending from the tropical Atlantic to East Asia. Moreover, a climate model successfully predicted the wet pattern in LRYR in short (1–5 month) lead real‐time predictions, and captured the observed statistical relationship between the winter precipitation in LRYR and the SSTA in the central tropical Pacific and equatorial Atlantic Oceans. These results indicate that such an event is predictable to some extent. [ABSTRACT FROM AUTHOR]

This study investigates the variability of winter precipitation over southern China (SC) and the impacts of sea surface temperature (SST) forcing in the tropical Pacific and Atlantic Oceans. The SC winter precipitation displays two major spatial modes: a same-sign mode with the maximum center located in southeastern China, and a north–south dipole mode with opposite variations between the lower reach of the Yangtze River and the southeastern coast of China. Both modes are associated with the variation of the western North Pacific (WNP) anticyclone. The same-sign mode is associated with canonical El Niño through the Pacific-East Asian teleconnection in the lower troposphere. The north–south dipole mode is subject to the impact of the western Pacific warm pool associated with the central Pacific El Niño and equatorial Atlantic forcing. The influence of equatorial Atlantic warming is through an upper-tropospheric zonal wave pattern from the tropical Atlantic to the mid-latitudes of East Asia. Both the tropical Pacific and Atlantic oceanic forcing induce a north–south dipole pattern of winter precipitation anomalies over SC by affecting the northward extension of the WNP anticyclone. Moreover, the observed statistical relationship between the dipole mode and the equatorial Atlantic SST anomalies is present in hindcasts/forecasts of the NCEP Climate Forecast System version 2 (CFSv2), confirming a potential source of the predictability of the north–south dipole-like variation of the winter precipitation in SC. [ABSTRACT FROM AUTHOR]

Persistent Heavy Rainfall (PHR) is the most influential extreme weather event in Asia in summer, and thus it has attracted intensive interests of many scientists. In this study, operational global ensemble forecasts from China Meteorological Administration(CMA) are used, and a new verification method applied to evaluate the predictability of PHR is investigated. A metrics called Index of Composite Predictability (ICP) established on basic verification indicators, i. e., Equitable Threat Score(ETS) of 24h accumulated precipitation and Root Mean Square Error(RMSE) of Height at 500hPa, are selected in this study to distinguish "good" and "poor" prediction from all ensemble members. With the use of the metrics of ICP, the predictability of two typical PHR events in June 2010 and June 2011 is estimated. The results show that the"good member"and"poor member"can be identified by ICP and there is an obvious discrepancy in their ability to predict the key weather system that affects PHR."Good member"shows a higher predictability both in synoptic scale and mesoscale weather system in their location, duration and the movement. The growth errors for "poor" members is mainly due to errors of initial conditions in northern polar region. The growth of perturbation errors and the reason for better or worse performance of ensemble member also have great value for future model improvement and further research. [ABSTRACT FROM AUTHOR]

The prediction skill and source of the predictability of the East Asian summer monsoon (EASM) system are examined in this work based on four state‐of‐the‐art seasonal climate forecast models including BCC_CSM1.1, ECMWF_SYS4, NCEP_CFS2 and TCC_CPS2. The prediction of the climatology and interannual EASM pattern and the impact on the prediction are further investigated. It is noted that the four models have some skill in predicting summer rainfall in the East Asia, however, the skill is low on average and also largely regional dependence. The interannual variation of EASM measured by monsoon circulation index is well reproduced, implying that the broad‐scale feature/pattern of EASM has higher predictability than the detailed spatial variation of EASM rainfall. The possible sources of predictability of the interannual variability of EASM are associated with the El Niño‐Southern Oscillation (ENSO) and the north Indian Ocean (NIO) sea surface temperature (SST) anomalies. The correlation pattern of rainfall with the NIO SST is characterized by a tripole pattern from south to north of East Asia, which is different from the correlation distribution of the southern‐northern dipole with ENSO, suggesting that NIO SST may exert influence on the EASM independently. The major biases in climatology of EASM in the models are the northward shift of the western Pacific subtropical high (WPSH) and weak monsoonal southerly over the coast of East Asia, which leads to the prediction bias of the Meiyu/Baiu/Changma (MBC) rainfall belt. The prediction of the interannual EASM pattern presents two deficiencies: too weak rainfall variability and northward shift of the dipole rainfall pattern (opposite variation between MBC and the northwestern Pacific), that may be caused by the biases of WPSH in the models. [ABSTRACT FROM AUTHOR]

This study examines the ability of the Beijing Climate Center Climate System Model (BCC_CSM) to predict the meridional pattern of summer precipitation over East Asia-Northwest Pacific (EA-NWP) and its East Asia-Pacific (EAP) teleconnection. The differences of summer precipitation modes of the empirical orthogonal function and the bias of atmospheric circulations over EA-NWP are analyzed to determine the reason for the precipitation prediction errors. Results indicate that the BCC_CSM could not reproduce the positive-negative-positive meridional tripole pattern from south to north that differs markedly from that observed over the last 20 years. This failure can be attributed to the bias of the BCC_CSM hindcasts of the summer EAP teleconnection and the low predictability of 500 hPa at the mid-high latitude lobe of the EAP. Meanwhile, the BCC_CSM hindcasts' deficiencies of atmospheric responses to SST anomalies over the Indonesia maritime continent (IMC) resulted in opposite and geographically shifted geopotential anomalies at 500 hPa as well as wind and vorticity anomalies at 850 hPa, rendering the BCC_CSM unable to correctly reproduce the EAP teleconnection pattern. Understanding these two problems will help further improve BCC_CSM's summer precipitation forecasting ability over EA-NWP. [ABSTRACT FROM AUTHOR]

Intraseasonal monsoon prediction is the most imperative task, but there remains an enduring challenge in climate science. The present study aims to provide a physical understanding of the sources for prediction of dominant intraseasonal modes in the East Asian-western North Pacific summer monsoon (EA-WNPSM): pre-Meiyu&Baiu, Changma&Meiyu, WNPSM, and monsoon gyre modes classified by the self-organizing map analysis. Here, we use stepwise regression to determine the predictors for the four modes in the EA-WNPSM. The selected predictors are based on the persistent and tendency signals of the sea surface temperature (SST)/2m air temperature and sea level pressure fields, which reflect the asymmetric response to the El Niño Southern Oscillation (ENSO) and the ocean and land surface anomalous conditions. For the pre-Meiyu&Baiu mode, the SST cooling tendency over the western North Pacific (WNP), which persists into summer, is the distinguishing contributor that results in strong baroclinic instability. A major precursor for the Changma&Meiyu mode is related to the WNP subtropical high, induced by the persistent SST difference between the Indian Ocean and the western Pacific. The WNPSM mode is mostly affected by the Pacific-Japan pattern, and monsoon gyre mode is primarily associated with a persistent SST cooling over the tropical Indian Ocean by the preceding ENSO signal. This study carries important implications for prediction by establishing valuable precursors of the four modes including nonlinear characteristics. [ABSTRACT FROM AUTHOR]

The boreal summer intraseasonal oscillation (BSISO) has two major activity centers, the northern Indian Ocean and tropical Western North Pacific, which dominate the monsoon intraseasonal variability over South Asia and East Asia, respectively. The spatial-temporal structures of BSISO over the Indian Ocean (10°S-30°N, 60°-105°E) (IOISO) and Western Pacific (10°S-30°N, 105°-150°E) (WPISO) are examined by corresponding the leading modes of daily OLR and 850-hPa zonal wind (U850). The IOISO features a northeastward propagation with a 30-45 days energy peak and the first principal component (PC1) has maximum variance in May, while the WPISO propagates northward with a broad spectral peak on 10-60 days and the PC1 has maximum variance in August. Because of the large regional differences, two regional indices, the IOISO index and WPISO index, are defined by their corresponding first two leading PCs. The combined IOISO-WPISO index captures about 30 % (10 %) of U850 (OLR) daily variance over the entire IO-WP region (10°S-30°N, 60°-150°E), which doubles that captured by the Madden-Julian Oscillation (MJO) index (Wheeler and Hendon ) and is 50 % higher than that captured by the BSISO index (Lee et al. ). The combined index also shows superior performance in representing biweekly and pentad-mean variations in the Asian-Pacific summer monsoon region (north of 10°N). The predictability/prediction skill and simulated principal modes of two regional BSISO indices are explored by using data derived from the Intraseasonal Variability Hindcast Experiment project. The major regional modes are reasonably well captured, but the forecasted fractional variances of the leading modes and variability center's locations exhibit significant deficiencies. The multi-model mean estimate of the predictability is 40-45 days for the IOISO index, whereas 33-37 days for the WPISO index. The less predictable WPISO is likely due to the existence of its significant biweekly component. The multi-model mean prediction skill is significantly higher with large initial amplitude (~20 days for two indices) than that with small initial amplitude (~11 days), suggesting that the prediction for development of BSISO is much more difficult than the prediction for mature BSISO disturbances' propagation. [ABSTRACT FROM AUTHOR]

The statistical relationship between observed interannual variability and the corresponding predictability of June-July-August mean precipitation is computed and discussed, using hindcasts from the Development of a European Multimodel Ensemble System for Seasonal to Interanuual Prediction (DEMETER) project and reanalysis datasets of the Global Precipitation Climatology Project and Climatic Research Unit. It is found that there exists a globally positive correlation between the variability and predictability, being prominent in the low latitudes and less evident in the high latitudes. Although this correlation varies with modulated verification datasets, multi-model ensembles and temporal periods, all of which are artificial factors, a significant positive correlation is found over East Asia, Australia, South America, Europe and Africa, while a negative correlation for North America, under most circumstances. Moreover, it is also found that different dynamical processes in the climate system seem to have made different contributions to building this 'larger variability-better predictability' relationship, possibly through enhancing the predictability over domains where there is a larger contribution to variability by the climate signals, while making an uncertain contribution to the predictability over domains that are less influenced by the signals. [ABSTRACT FROM AUTHOR]

Interdecadal changes in the potential predictability of the East Asian summer monsoon (EASM) and South Asian summer monsoon (SASM) were investigated using the signal‐to‐noise ratio of daily reanalysis data from 1948 to 2017. Results reveal that variations in the SASM predictability are out of phase with those in EASM predictability, and that SASM predictability is higher (lower) than EASM predictability before (after) the 1980s. The out‐of‐phase relationship of the predictability of the two monsoon systems is attributed mainly to temporal changes in their relationships with El Niño‐Southern Oscillation (ENSO). The relationship between the EASM and ENSO is related to the position of the ENSO heating center, which is located in the central Pacific before the 1980s but shifted to the eastern Pacific after the 1980s, accompanied by a strengthening (weakening) in the relationship between ENSO and the EASM (SASM). The shift in the position of the ENSO heating center was found to have contributions from the modulation of the Pacific decadal oscillation. (a) Interdecadal change in the potential predictability of the EASM and the SASM from 1948 to 2017 using an 11‐year sliding window, where red lines indicate the EASM and blue lines indicate the SASM. (b) and (c) Same as (a) but for signal and noise. [ABSTRACT FROM AUTHOR]