Recent observational and modeling studies have demonstrated the substantial influence of the Tibetan Plateau (TP) spring land surface temperature (LST) and subsurface temperature (SUBT) on downstream summer droughts/floods events in East Asia, highlighting the potential application of LST/SUBT on sub-seasonal to seasonal prediction (S2S). In this study, we employ the National Centers for Environment Prediction—Global Forecast System/Simplified Simple Biosphere model version 2 (GFS/SSiB2) to investigate the potential role of the late spring warm LST anomaly over the TP on the extraordinary June 1998 flood in the south of the Yangtze River region. Numerical experiments indicate that the warmer (above normal) May LST over the TP may contribute to the extreme flood of 1998 over the south of the Yangtze River region, with the LST reproducing about 57% and 64% of observed above-normal rainfall anomaly over the south of the Yangtze River region and southeastern China, respectively. Further analyses reveal a possible effect of springtime TP’s LST on summer southern and eastern Asian rainfall and identify some hot spots, suggesting that the TP’s spring LST effect is not only limited to the Yangtze River region, but to a much larger scale. The imposed warm LST/SUBT over the TP triggers a strong wave activities propagating eastward along the upper-level westerly jet, associated with an increase of the atmospheric baroclinic instability as well as a strengthening and southeastward movement of the South Asian high, leading to intensified moisture convergence and convective instability favorable to the excessive rainfall in the downstream region of East Asia. The results of the 1998 case have also been compared with the results from year of 2003, which had a very cold spring LST anomaly over the TP and a severe downstream June 2003 drought (flood) in southern (northern) of the Yangtze River Basin area. Simulation results provide further evidence of the great importance of the TP spring land surface temperature anomaly in regulating summer extreme hydroclimatic events (e.g. droughts and floods) in South and East Asia. The present study suggests that consideration of LST/SUBT anomalies has a strong potential for more skillful S2S prediction of extreme hydroclimatic events such as floods, droughts and heatwaves over both Southern and Eastern Asia.

Subseasonal-to-seasonal (S2S) precipitation prediction in boreal spring and summer months, which contains a significant number of high-signal events, is scientifically challenging and prediction skill has remained poor for years. Tibetan Plateau (TP) spring observed surface temperatures show a lag correlation with summer precipitation in several remote regions, but current global land-atmosphere coupled models are unable to represent this behavior due to significant errors in producing observed TP surface temperatures. To address these issues, the Global Energy and Water Exchanges (GEWEX) program launched the "Impact of Initialized Land Temperature and Snowpack on Subseasonal-to-Seasonal Prediction"(LS4P) initiative as a community effort to test the impact of land temperature in high-mountain regions on S2S prediction by climate models: more than 40 institutions worldwide are participating in this project. After using an innovative new land state initialization approach based on observed surface 2-m temperature over the TP in the LS4P experiment, results from a multimodel ensemble provide evidence for a causal relationship in the observed association between the Plateau spring land temperature and summer precipitation over several regions across the world through teleconnections. The influence is underscored by an out-of-phase oscillation between the TP and Rocky Mountain surface temperatures. This study reveals for the first time that high-mountain land temperature could be a substantial source of S2S precipitation predictability, and its effect is probably as large as ocean surface temperature over global "hotspot"regions identified here; the ensemble means in some "hotspots"produce more than 40% of the observed anomalies. This LS4P approach should stimulate more follow-on explorations.