Introduction

Summer is the rainy season in East and South Asia. However, in 2022, the climatic pattern deviated from normal, showing contrasting extreme events in East and South Asia. The Yangtze River valley (YRV), which extends from the eastern Tibetan Plateau to coastal Shanghai, has suffered its most violent and persistent heat wave since 1979. Meanwhile, in Pakistan-northwestern India region, an unprecedented amount of rainfall occurred, causing severe flooding along the Indus Basin (IB). This “YRV drought and IB flood” pattern reflects the first MV-EOF mode of the surface air temperature (SAT) and precipitation over subtropical Asia (Supplementary Fig. 1), which accounts for about 17% of the total variance. The average temperature (precipitation) exceeds the climatology (1991–2020) mean by 2 K (150 mm) (Fig. 1a), which could equal as close as 2.5 standard deviations (σ) in 2022 (Fig. 1b). The co-occurrence of “seesaw” type extremes has posed major food security and health risks1,2.

Fig. 1: July–August 2022 climatic anomalies relative to the 1991–2020 climatology.
figure 1

a The column-integrated moisture flux (UqVq, vector, kg·m–1·s–1), Z500 (The green and orange contours represent 5880-gpm isoline for 2022 and climatology), surface air temperature (SAT; red shading, K), and precipitation (green shading, mm·month–1) anomalies in July–August 2022. The purple boxes imply the Yangtze River Valley (YRV, 27°N–33°N, 105°E–123°E) and Indus Basin (IB, 20°N–35°N, 60°E–80°E) domains. c SST (shading, K), UV850 (vector, m·s–1), and Z1000 (contour, m). The shading, vector, and black contour represent the region with anomalies’ magnitude >1σ. The red boxes outline the tropical central-eastern (12°S–10°N, 160°E–260°E) and western (10°S–3°N, 95°E–140°E) Pacific. The green box frames the southern Indian Ocean (30°S–10°S, 40°E–90°E). Normalized time series of July–August b SAT averaged over YRV and precipitation averaged over IB, d Niño3.4 and zonal gradient indices from 1979 to 2022. Blue and red lines denote –0.5 and –0.8σ.

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Concurrent with the “seesaw” extremes, the 5880-gpm contour dominates the mid-troposphere over the entire YRV, implying a pronounced westward expansion of the western Pacific subtropical high (WPSH) compared to its climatology mean (Fig. 1a). The resulting descending motion enhances incoming surface solar radiation by reducing cloud cover and facilitating the occurrence of heat waves3,4. A large amount of anomalous lower-level southeasterly wind on the south flank of the WPSH extends to Pakistan-northern India5, conveying moisture air from the Bay of Bengal to fuel the increased IB rainfall (Fig. 1c).

The WPSH controls the summer climate over Asia, its variability can be modulated by the atmospheric and boundary layer forcings7c), by about one-third (0.55 vs. 1.9; 28%) in August (Fig. 7d). The underestimated magnitude of the “YRV drought and IB flood” seesaw index suggests that other physical processes independent of the tropical Pacific zonal SST gradient, the southern Indian Ocean and Barents-Kara Sea forcings were at work as well. For instance, Liu et al. emphasized that the 2022 YRV heatwave was also embedded in intra-seasonal oscillation (ISO)51. The relationship of ISO with extreme Indus Basin rainfall deserves further investigation. Moreover, as revealed by the previous study, the surface climatic conditions also contributed by local land-atmosphere interaction52,53. Detecting the potential local positive feedback that maintains the extreme IB rainfall or YRV surface temperature is also of significant interest.

Methods

Reanalysis datasets

The Monthly Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5) data54 for 1979–2022 was obtained from the National Oceanic and Atmospheric Administration (NOAA), with a horizontal resolution of 2° × 2°; The monthly precipitation data for 1979–2022 from NOAA’s Land Precipitation Reconstruction (PREC/L) data set55; The monthly surface air temperature data on a 0.5° × 0.5° grid from the Global Historical Climatology Network version 2 and the Climate Anomaly Monitoring System (GHCN_CAMS)56 for 1979–2022; and the monthly atmospheric reanalysis data on a 1.5° × 1.5° grid from the fifth-generation of ECMWF global atmospheric reanalysis (EAR5) data set57 for 1979–2022.

Methodology

In this study, the main statistical methods include correlated coefficient analysis, linear regression analysis, and composite analysis. The statistical significance test is based on a two-tailed Student’s t test with N–2 degree of freedom (N is the number of years). Monthly anomalies refer to the deviations from the climatological mean (1991–2020) with the linear trend removed.

We established the covariance matrix of the surface air temperature (SAT) and precipitation anomalies in subtropical Asia (20°N–40°N, 105°E–123°E) from 1979 to 2022 to perform the multivariate empirical orthogonal function (MV-EOF) analysis.

The Niño-3.4 and Niño-4 indices were obtained from the Climate Prediction Center. The zonal gradient index is defined as the SST difference between the tropical central Pacific (12°S–10°N, 160°E–260°E) and tropical western Pacific (10°S–3°N, 95°E–140°E). And the normalized precipitation and SAT averaged over (20°N–35°N, 60°E–80°E) and (27°N–33°N, 105°E–123°E) were defined as the Indus Basin rainfall and YRV SAT indices.

To assess the impacts of the tropical and extra-topical SST forcing, the 5th generation European Center-Hamburg model (ECHAM5.4)58, was used for the numerical experiment. The ECHAM5.4 model was developed by the Max-Plank Institute and has been widely used in previous studies to understand the impact processes and mechanisms of ENSO and Arctic sea ice/SST15,21,22,23,59. All the simulations utilized the triangular 63 horizontal resolution (1.9° × 2.5°) with 19 vertical levels. The control (CTRL) experiment was driven by the observed climatology SST. We carried out 40-year integration and the last 30 years were extracted for analysis. The sensitivity experiments were integrated for 30 years with initial conditions obtained from the CTRL run and the specific SSTA in July–August 2022 prescribed onto the July and August climatological SST.

The linear baroclinic model (LBM) was also performed to investigate the linear response of the circulation anomaly over Eurasia to the Indus Basin diabatic heating anomaly with 128 × 64 horizontal grids and 20 sigma vertical levels. This model was developed by the Center for Climate System Research at the University of Tokyo and the National Institute for Environmental Studies in Japan60. The forcing of the diabatic heating is parameterized by the observational July precipitation anomalies over the Indus Basin region (20°–35°N, 60°–80°E). We integrated the LBM for 30 days and averaged outputs for the last 15 days as the stationary atmospheric responses. The added forcings are displayed in Supplementary Fig. 7 in supplementary.

Considering the seesaw pattern was impacted by different extra-tropical forcings in the two months, we quantitatively diagnose the contribution of the SST gradient over the tropical Pacific Ocean and extra-tropical forcings in July and August, respectively. To represent the variability of the seesaw pattern, we defined a “YRV drought and IB flood” seesaw index as:

$${\rm{YISI}}=0.5\times ({\rm{YRV\; SAT\; index}}+{\rm{IB\; rainfall\; index}})$$
(1)

We construct the multi-regression model based on the standardized zonal gradient (ZGI), southern Indian Ocean (SIOI), and Barents-Kara Sea (BKSI) indices:

$${{\rm{YISI}}}_{{Jul}}=-0.36\times {{\rm{ZGI}}}_{{Jul}}-0.1\times {{\rm{SIOI}}}_{{Jul}}$$
(2)
$${{\rm{YISI}}}_{{Aug}}=-0.11\times {{\rm{ZGI}}}_{{Aug}}-0.28\times {{\rm{BKSI}}}_{{Aug}}$$
(3)