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According to twenty-first century climate-model projections, greenhouse warming will intensify rainfall variability and extremes across the globe. However, verifying this prediction using observations has remained a substantial challenge owing to large natural rainfall fluctuations at regional scales. Here we show that deep learning successfully detects the emerging climate-change signals in daily precipitation fields during the observed record. We trained a convolutional neural network (CNN) with daily precipitation fields and annual global mean surface air temperature data obtained from an ensemble of present-day and future climate-model simulations. After applying the algorithm to the observational record, we found that the daily precipitation data represented an excellent predictor for the observed planetary warming, as they showed a clear deviation from natural variability since the mid-2010s. Furthermore, we analysed the deep-learning model with an explainable framework and observed that the precipitation variability of the weather timescale (period less than 10 days) over the tropical eastern Pacific and mid-latitude storm-track regions was most sensitive to anthropogenic warming. Our results highlight that, although the long-term shifts in annual mean precipitation remain indiscernible from the natural background variability, the impact of global warming on daily hydrological fluctuations has already emerged. 

This study examined the contribution of the Pacific decadal oscillation (PDO) to the record-breaking 2013–17 drought in the Korean Peninsula. The meteorological drought signal, measured by the Standardized Precipitation Index (SPI), in 2013 and 2016 co-occurred with a heat wave. The positive phase of the PDO during the mid-2010s was responsible for the precipitation deficit, particularly in 2014, 2015, and 2017, resulting in 5 years of meteorological drought. The enhanced atmospheric heating anomalies over the subtropical central Pacific, induced by the in situ PDO-related sea surface temperature (SST) warming, led to a low-atmospheric cyclonic flow centered over the midlatitude Pacific. The northerly wind anomalies at the western edge of this low-level cyclonic flow were responsible for the horizontal negative advection of moist energy, which contributed to the decreased precipitation and the resultant negative SPI over the Korean Peninsula in 2014, 2015, and 2017. The large-ensemble simulation supported the observational findings that the composited SST anomalies during the 5 years of persistent drought exhibited prominent and persistent SST warming over the subtropical central Pacific, along with large-scale cyclonic flow over the North Pacific. The findings of this study imply that the SST anomalies over the North Pacific and subtropical central Pacific can be a predictable source to potentially increase the ability to forecast multiyear droughts over the Korean Peninsula.

 

The season‐dependent impacts of the tropical North Atlantic (TNA) sea surface temperature anomaly (SSTA) on subsequent El Niño‐Southern Oscillation (ENSO) evolution were investigated through observational and modeling studies. The results indicate that, although the maximum amplitude of the TNA SSTA occurs during boreal spring, the TNA SSTA in boreal summer generates a stronger rainfall response in situ, which can further induce a significantly stronger zonal wind anomaly over the equatorial western Pacific via Kelvin and Rossby wave processes. The cause of a stronger precipitation response in boreal summer is attributed to the northward migration of the climatological Atlantic warm pool and the Inter‐Tropical Convergence Zone. Idealized Coupled General Circulation Model experiments further demonstrate that a persisting TNA SSTA forcing up to boreal summer is critical in conveying the TNA impact to subsequent ENSO evolutions in the Pacific.

 

This study examined the origin of the systematic underestimation of rainfall anomalies over East Asia during July–August 2020 in operational forecasts. Through partial nudging experiments, we found that the East Asian rainfall anomalies were successfully predicted in GloSea5 with corrected tropical sea surface temperature (SST) forcing. Once the observed SST is applied over the Indian Ocean and tropical central‐eastern Pacific, a low‐level anticyclonic anomaly over the subtropical western Pacific, which transports warm‐moist air from the tropics to increase the East Asian precipitation, is well reproduced as observed. By further separating the SST into climatological and anomalous components, we revealed that the cold and dry mean state bias over the Indian Ocean and central‐eastern Pacific is responsible for the weak anomalous atmospheric teleconnection patterns from the tropics to East Asia. This implies that correcting the model mean climatological fields can directly impact the operational seasonal forecast skill.

 

The El Niño–Southern Oscillation (ENSO), which originates in the Pacific, is the strongest and most well-known mode of tropical climate variability. Its reach is global, and it can force climate variations of the tropical Atlantic and Indian Oceans by perturbing the global atmospheric circulation. Less appreciated is how the tropical Atlantic and Indian Oceans affect the Pacific. Especially noteworthy is the multidecadal Atlantic warming that began in the late 1990s, because recent research suggests that it has influenced Indo-Pacific climate, the character of the ENSO cycle, and the hiatus in global surface warming. Discovery of these pantropical interactions provides a pathway forward for improving predictions of climate variability in the current climate and for refining projections of future climate under different anthropogenic forcing scenarios.