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By observing well‐known sea surface temperature (SST) indices and outputs from Climate Forecast System version 2 (CFSv2) hindcasts and predictions, in this study, we developed a hybrid statistical downscaling prediction model (HSDPM) based on a timescale decomposition approach, which effectively improved the forecasting ability of summer (June–August) precipitation over Hainan Island (HNSP) in China; this precipitation presented multiple obvious timescale variabilities. We found that the interannual variability of the HNSP is closely related to the observed North Atlantic tripole SST anomalies in the preceding winter and the summer sea level pressure over the South China Sea by CFSv2 predicted in March. On the other hand, the interdecadal variability in the HNSP is linked to the prewinter Pacific Interdecadal Oscillation (PDO) according to the observations, the CFSv2 predictions of the summer surface air temperature over the tropical central‐east Pacific. On this basis, both the interannual and interdecadal components of the HNSP are effectively predicted using the corresponding predictors via multiple regression; thus, the HSDPM is ultimately established by combining the above two components. Compared with the original CFSv2 model, the HSDPM model achieves a considerable improvement in performance in predicting the HNSP. Specifically, the temporal correlation coefficient, ratio of root‐mean‐square error and anomaly sign consistency rate between the observed and HSDPM‐predicted HNSPs are 0.70, 17.4% and 80.5%, respectively, which are significantly greater than the above three forecasting index values of 0.23, 38.3% and 48.8%, respectively, obtained from the original CFSv2 predictions. The application of the HSDPM may be beneficial for drought and flood prevention and mitigation in Hainan Island. [ABSTRACT FROM AUTHOR]

Northeast China (NEC) is located between the subtropical monsoon and temperate-frigid monsoon regions and exhibits two successive rainy seasons with different natures: the northeast cold vortex rainy season in early summer (May–June) and the monsoon rainy season in late summer (July–August). Summer rainfall over NEC (NECR) has a fundamental influence on society, yet its successful seasonal prediction remains a long-term scientific challenge to current dynamical models. The poor NECR prediction skill is partly attributed to the large NECR variability at both the interannual and interdecadal time scales. Here, we focus on the oceanic drivers of the late summer NECR variability and associated physical processes at interannual time scale. Then, we establish an empirical prediction model to predict the interannual variability of summer NECR at 1-month lead time (in June). The analysis of observations spanning 40 years (1963–2002) reveals three physically and synergistically influencing predictors of the late summer NECR interannual variability. Above-normal NECR is preceded in the previous spring by (a) warm sea surface temperature (SST) anomalies in the tropical northern Indian Ocean, (b) a positive thermal contrast tendency in the tropical West–East Pacific SST, and (c) a positive tendency of the North Atlantic tripolar SST. These precursors enhance the anomalous low-level anticyclone over the western North Pacific and southerly anomalies over NEC in late summer, which are beneficial to enhancing NECR. An empirical prediction model built on these three predictors achieves a forecast temporal correlation coefficient (TCC) skill of 0.72 for 1961–2019, and a 17-year (2003–2019) independent forecast shows a significant TCC skill of 0.70. The skill is substantially higher than that of five state-of-the-art dynamical models and their ensemble mean for 1979–2019 (TCC = 0.24). These results suggest that the proposed empirical model is a meaningful approach for the prediction of NECR, although the dynamical prediction of NECR has considerable room for improvement. [ABSTRACT FROM AUTHOR]

In this study, the summer rainfall patterns in the East China monsoon region during 1951-2015 were objectively classified into four typical categories: the northern China rainfall pattern (NCP), the intermediate rainfall pattern (IRP), the Yangtze River rainfall pattern (YRP), and the South China rainfall pattern (SCP). The periods of the four patterns show significant decadal characteristics. The NCP occurred mainly between the late 1950s and the early 1980s, and the IRP in the late 1950s to the early 1970s and the 2000s. The YRP occurred mainly between the 1980s and the 1990s, and the SCP between the mid-1990s and the early 21st century. The relationship between the East Asian summer monsoon index (EASM I ) and the four rainfall patterns was comparatively analyzed. The results confirmed that the four rainfall patterns have obvious differences in the EASM. In the NCP, IRP, or SCP years, the EASM I primarily showed a positive phase and a strong summer monsoon; in the YRP years, the EASM I primarily showed a negative phase and a weak summer monsoon. [ABSTRACT FROM AUTHOR]

Based on the 1983∼2011 CMAP data, the precipitation anomaly in East Asia and its nearby sea regions (hereafter called East Asia for short) demonstrates the '+−+' pattern before 1999 and the '−+−' pattern afterwards; this decadal change is contained principally in the corresponding EOF3 component. However, the NCC_CGCM forecast results are quite different, which reveal the '+−+−' pattern before 1999 and the '−+−+' pattern afterwards. Meanwhile, the probability of improving NCC_CGCM's forecast accuracy based on these key SST areas is discussed, and the dynamic-statistics combined forecast scheme is constructed for increasing the information of decadal change contained in the summer precipitation in East Asia. The independent sample forecast results indicate that this forecasting scheme can effectively modify the NCC_CGCM's decadal change information contained in the summer precipitation in East Asia (especially in the area of 30°N-55°N). The ACC is 0.25 and ACR is 61% for the forecasting result based on the V SST area, and the mean ACC is 0.03 and ACR is 51% for the seven key areas, which are better than NCC_CGCM's system error correction results (ACC is −0.01 and ACR is 49%). Besides, the modified forecast results also provide the information that the precipitation anomaly in East Asia mainly shows the '+−+' pattern before 1999 and the '−+−' pattern afterwards. [ABSTRACT FROM AUTHOR]

In this study, we investigate the interdecadal variation characteristics of the dominant mode of summer precipitation over East Asia‐Northwest Pacific (EA‐NWP) before and after late 1990s, reveal the physical mechanism, and address its possible connection with the regional ocean warming background. Study results show that the empirical orthogonal function mode 1 (EOF1) of summer precipitation anomalies over EA‐NWP during the global ocean warming acceleration period (1979–1998) present a more significant dipole pattern between Yangtze River Basin (YRB) and tropical NWP compared with that during the global ocean warming slowdown period (1999–2012). And then we find that the anomalous dipoles of low‐level winds, geopotential height, convection, and associated wave activity fluxes over NWP significantly shift westward (eastward) and enhance (weaken) in 1979–1998 (1999–2012), which are regarded as the direct cause of the adjustment of dipole pattern between YRB and tropical NWP. Further analysis reveals that the negative sea surface temperature (SST) anomalies in the tropical NWP are prominent (not prominent), resulting in the stronger (weaker) Pacific–Japan Teleconnection over Japan and Tropics in NWP during the period 1979–1998 (1999–2012), and ultimately lead to the change of summer precipitation EOF1 in EA‐NWP. By regressing vertical velocity, outgoing longwave radiation, relative vorticity, wave activity fluxes, and precipitation onto the NWP SST in the two periods, the impacts of the SST in the NWP on the interdecadal variation of summer precipitation EOF1 in EA‐NWP are verified. Finally, the mechanism diagram of interdecadal variation of summer precipitation is given, which can be useful for short‐term precipitation prediction of East Asia. [ABSTRACT FROM AUTHOR]

By using the one-month‑lead predictions from the Climate Forecast System version 2 (CFSv2), this study evaluates the predictability of the unprecedented heatwaves hitting the Yangtze River Valley (YRV) and Tibetan Plateau (TP) in 2022 late summer. CFSv2 successfully reproduces the central China heatwaves, but shows distinct performances between the YRV and TP. Specifically, CFSv2 exhibits stronger ability to capture TP heatwaves that is close to the highest historical prediction record, whereas the predicted extremity of the YRV heatwaves is underestimated compared to the observations. The accurate simulation of the intense upper-level circumglobal teleconnection (CGT) pattern, particularly for the East Asia CGT center (CGT EA), is essential for the success predictions of high temperature anomalies, due to the strong and accurate linkage between CGT EA and YRV/TP heatwaves in CFSv2. The intense CGT pattern is associated with a stronger model response to developing La Niña in CFSv2, quite different from the observed leading contribution from the unprecedented Pakistan rainfall. The impacts of developing La Niña and Pakistan rainfall on the intense CGT pattern are significantly overestimated and underestimated by CFSv2, respectively. Additionally, the La Niña-forced enhancement of lower-level western Pacific subtropical high (WPSH) plays another important role in YRV heatwaves, but the reproduction of WPSH intensity is unable to reflect YRV heatwaves in CFSv2 due to the southeastward shift of the predicted WPSH location, which should be responsible for the underestimated YRV heatwaves. The model improvements in aspect of the Pakistan rainfall, El Niño-Southern Oscillation–CGT linkage and lower-level WPSH location should help effectively predict the central China heatwaves in the future. • CFSv2 successfully predicts the central China heatwaves in 2022 late summer. • The accurately predicted CGT is essential for the reproduction of heatwaves. • La Niña leads to the intense CGT in CFSv2 rather than Pakistan rainfall in observation. [ABSTRACT FROM AUTHOR]

In this paper, we investigated the spatio-temporal characteristics of the amount of light rain and the number of days with light rain in the ESWC and explored the possible causes of the significant decrease in the number of days with light rain. The results show that: (1) Both the amount of light rain and the number of days with light rain over the ESWC in summer showed a decreasing trend, with linear trends of −1.5 mm/decade and −0.88 day/decade, respectively. The decrease in the number of days with light rain was more distinct than the decrease in the amount of light rain. (2) The decrease in the number of days with light rain was closely related to the decrease in the relative humidity caused by an increase in temperature and specific humidity. The trend in relative humidity resulting from an increase in temperature alone was −1.1%/decade, whereas the trend in relative humidity caused by an increase in specific humidity alone was 0.29%/decade. The trend in relative humidity was −0.76%/decade. The decrease in relative humidity was mainly a result of an increase in temperature. (3) The increase in temperature resulted in a decrease in the relative humidity and an increase in the surface evaporation. The increase in evaporation increased the specific humidity, which, in turn, increased the relative humidity. This compensated for the decrease in the relative humidity caused by the increase in the temperature and further decelerated the decrease in the number of days with light rain. • The number of summer days with light rain in the ESWC decreased significantly during 1961–2018. • The decrease in the number of days with light rain was closely related to the decrease in the relative humidity. • The trend of relative humidity made by an increase in temperature alone was −1.1%/decade. • The increase in evaporation compensated for the decrease in relative humidity caused by the increase in temperature. [ABSTRACT FROM AUTHOR]