A dominant mode in the first phase of the Asian summer monsoon rainfall: role of antecedent remote land surface temperature

The first/initial phase (during May to June) of the Asian summer monsoon (ASM), primarily driven by land-sea thermal gradient, varies from year to year and enormously affects people’s livelihood and the economy of this region. Moreover, the first phase, associated with the sub-seasonal variability (days to weeks), witnesses many extreme hydroclimatic events. Therefore, it is crucial to understand the sources of predictability of the initial phase of the ASM. Here we identify a dominant mode of variability in June rainfall over the entire Asian monsoon region. This mode is found to be linked with the spring (April, May) land surface temperature (LST) of the areas centred around the Western Third Pole (WTP). The Third Pole is the high elevation area centred on the Tibetan plateau. The WTP region is also home to many glaciers and steep mountains, including the second-highest peak in the world (i.e. Karakorum range). Consequently, spring LST has a strong inverse relationship with snow water equivalent (r = - 0.65) over WTP, suggesting a seminal role of land surface processes in the first phase of ASM variability. The observed dominant modes and their teleconnections are also investigated in the 30-years re-forecast by five global coupled climate models participating in the “Impact of Initialized Land Surface Temperature and Snowpack on Sub-seasonal to Seasonal Prediction phase I” project (LS4P-I; Xue et al. (Geosc Model Devel 14(7):4465–4494, 2021; Bul Amer Meteor Soc 103: E2756-E2767, 2022)). While most models faithfully reproduce the observed link of June rainfall over South Asia with the remote LST, all models fail to capture the same over east Asia. In general, models show a significant bias in simulating the LST and the dominant modes of rainfall variability. Our findings may improve the understanding of the Asian summer monsoon variability and predictability, which may help improve the dynamical sub-seasonal to seasonal forecast system.