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We used AISMR data from the Indian Institute of Tropical Meteorology (IITM) to represent the variability of the Indian summer monsoon rainfall (June to September). This dataset was derived from the area-weighted average of 306 rain-gauge stations that are almost uniformly distributed throughout India (50). To confirm the reliability of the IITM data, we also used rainfall datasets from the Climatic Research Unit Time Series (0.5° × 0.5°;CRU TS v4.00) (51) and the Global Precipitation Climatology Centre (0.5° × 0.5°; GPCC v7) (52).

The snow cover data were from the Northern Hemisphere EASE-Grid 2.0 weekly snow cover and sea ice extent version 4, available at the National Snow and Ice Data Center (NSIDC). The data were stored as binary files in arrays of 720 columns by 720 rows and had a spatial resolution of 25 km. To make the comparison with other data easier, we converted the EASE-Grid 2.0 data to a 1° × 1° longitude-latitude grid. The snow cover data were in binary form (snow or no snow), and so we used the SCF, rather than snow cover extent, to represent the variability of snow cover. The monthly SCF was calculated by summing the number of weeks in that month for which snow was present for a pixel and by expressing this number as a fraction of the total number of weeks in that month (16). In this study, we used only the spring (March, April, and May) snow cover to investigate the snow-monsoon relationship. There are two reasons for this restriction. First, the snow cover has little interannual variability during the winter. Second, the snow-albedo effect was found to be high in the spring because of the rapid retreat of snow cover. In addition, the snow-hydrological effect, controlled by anomalies in snow water equivalent, emerges in spring, meaning that the degree to which the atmosphere responds to changes in snow should be strong at this time of year (4, 5).

The data of 500 hPa air temperature and wind fields with a resolution of 2.5° × 2.5° for the summer months (June to September) were obtained from the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (53). The air temperature and wind data from the JRA-55 were also used (54). The soil moisture data were provided by the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) and had a resolution of 0.5° × 0.5°. In addition, we used the 3-month mean (June, July, and August) Oceanic Niño Index in the Niño 3.4 region to represent the ENSO, and the average monthly NAO index for January, February, and March to represent the cold season NAO index. Both the ENSO index and the NAO index are also available at the NOAA CPC. We used summer (June to September) Dipole Mode Index (DMI) from the NOAA Earth System Research Laboratory to represent the IOD. The IOD was considered since it exerts an important impact on both the Indian summer monsoon rainfall (55) and Eurasian snow cover (56).

The snow cover and precipitation data from 30 earth system models participating in the CMIP5 were also used in our study (table S1). Because of the uncertainties in the climate forcing data for CMIP5 after 2005 (57), we only used the historical simulations data (1967–2005). The CMIP5 models had various spatial resolutions, and so, for convenience, we converted all the CMIP5 model output to a 1° × 1° longitude-latitude grid.

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