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1 doi: /nature Trend patterns of SST and near-surface air temperature Bucket SST and NMAT have a similar trend pattern particularly in the equatorial Indo- Pacific (Fig. S1), featuring a reduced zonal gradient. This similarity in trend pattern is supported by the CMIP3 twentieth century experiments (Fig. S2). 59 of 71 (83%) ensemble members exceed the spatial correlation of 0.9, and 67 of 71 (94%) exceed The mean and median values of spatial correlations are and 0.946, respectively, statistically significant at P < Figure S1 Trend patterns of bucket-sampled SST and nighttime marine air temperature (NMAT) for a, ICOADS bucket SST, b, ICOADS NMAT, and c, MOHMAT43N. Although the MOHMAT43N dataset ends in 2006, the trends are scaled to 60-year changes. Stippled indicates regions of trends not exceeding the 95% confidence level. Figure S2 Histogram of spatial correlations between SST and near-surface air temperature trend patterns in CMIP3 twentieth century experiments. Each trend is calculated over tropical oceans (30 S 30 N) for The mean and median values of spatial correlations are and 0.946, respectively (both are significant P < 0.01). 1

2 2. SLP and SST changes in atmospheric reanalyses HadSLP2 (ref.1) has widely been used to describe long-term changes in SLP over the globe. It shows increasing trends over the Maritime Continent and decreasing trends east of the Philippines and over the central to eastern tropical Pacific for (Fig. S3). The magnitude of HadSLP2 trends is slightly weaker than but its pattern is quite similar to that of ICOADS 2, (Fig. 1a in the main text). In contrast, the ERA 40 (ref.3) and 20CRv2 (ref.4) reanalyses show negative SLP trends over the Maritime Continent, with a meridional ridge of SLP trends over the central and eastern equatorial Pacific. These SLP trend patterns look consistent with a SST warming minimum zonally elongated in the central to eastern equatorial Pacific and strong warming in the equatorial Indo-western Pacific. Similar patterns of SLP and SST changes are found in the NCEP-NCAR reanalysis 5 (Fig. 1f in the main text), although the SST datasets may have large uncertainty of the warming magnitude in the equatorial Indo-western Pacific (Fig. S4). In the NCEP-NCAR (ERA-40) reanalysis, the Global sea-ice and Sea Surface Temperature version 2.2 (ref.6) (HadISST1; ref.7) from the UK Met Office was used prior to 1982 (November 1981) and Reynolds SST (ref.8) (NOAA/NCEP 2D-Var SST dataset; ref.9) for later years. The 20CRv2 reanalysis uses HadISST1. Figure S3 Patterns of SST and SLP changes. As in Figure 1 in the main text, but for a, MST-HadSLP2, b, ERA-40, and c, 20CRv2. All trends are the annual mean and scaled to 60-year changes. 2

3 3. Uncertainty in HadSST3 trends Figure S4a shows a spatial pattern of the leading EOF for the tropical Indo-Pacific SST trends (30 E 60 W, 32.5 S 32.5 N) from 100 HadSST3 realizations 10,11. The leading EOF indicates large uncertainty in the warming magnitude over the equatorial Indo-western Pacific, accounting for 83.6% of the total variance. The principal component of the leading EOF indicates that HadSST3 realizations #01 and #12 are upper and lower extremes, respectively (Fig. 4 in the main text). Using these two realizations, we perform ECHAM5 (ref.12) experiments to estimate uncertainties in simulated atmospheric response to SST warming patterns. Figure S4b shows their difference in 850hPa geopotential height and precipitation in June July. The large uncertainty in equatorial Indo-western Pacific warming likely excites a meridional wave train in the tropical western Pacific East Asian sector, with positive (negative) precipitation in north (south) China. This pattern is well known as the Pacific-Japan teleconnection strongly affecting the Meiyu-Baiu rainband during the early summer in East Asia 13,14. Coincidentally this rainfall pattern over China, with signs reversed, is reminiscent of observed change for the past six decades 15,16. We further note that this meridional wave train is observed in interannual variability during the summer when the Indian Ocean is anomalous warm 17. Figure S4 Uncertainty of HadSST3 trends and its impact on regional climate. a, The leading EOF for the tropical Indo-Pacific trend patterns for from HadSST3 realizations. b, Difference in June July precipitation (mm mon -1 per 60 years; shading) and 850hPa geopotential height (contours at 0, ±2.5, ±5, ± m per 60 years) between ECHAM5 experiments forced with the HadSST3 realizations #12 and #01. Stippled indicates regions of significant precipitation difference exceeding the 95% confidence level. Positive (negative) contours are solid (dashed) lines, and zero contours are thickened. 3

4 4. SST warming patterns in CMIP5 multi-model ensemble means Figure S5 shows patterns of SST change in the multi-model ensemble means of the CMIP5 historical run, RCP4.5, and RCP8.5 scenarios. SST changes were calculated as minus for the historical run, and minus for RCP4.5 and RCP8.5 scenarios. All simulations are characterized by local warming maxima in the equatorial Pacific, the subtropical North Pacific and the northwestern tropical Indian Ocean, and warming minima in the subtropical South Pacific and South Indian Ocean. The magnitude of reduction in ΔSST, which is defined as the zonal difference between the eastern equatorial Pacific ( W, 5 S-5 N) and western equatorial Pacific/eastern Indian Ocean ( E, 5 S-5 N), increases from the historical run (0.134 C/100yr) through RCP4.5 (0.205 C/100yr) to RC8.5 (0.345 C/100yr) as their GHG forcing intensifies. Figure S5 SST warming patterns of CMIP5 simulations. a, Historical run ( minus ), b, RCP4.5 ( minus ), and c, RCP8.5 ( minus ) scenarios. ΔSST change ( C/100yr) is indicated at the top-right corner of each panel. 22 models (BCC-CSM1-1, CanESM2, CCSM4, CNRM-CM5, CSIRO-Mk3.6.0, FGOALS-s2, FGOALS-g2, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, GISS- E2-R, HadGEM2-ES, INM-CM4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC5, MIROC-ESM, MIROC-ESM- CHEM, MPI-ESM-LR, MPI-ESM-MR, MRI-CGCM3, and NorESM1-M) were used for historical and RCP8.5 simulations, and 22 models (ACCESS1.0, BCC-CSM1-1, CanESM2, CCSM4, CNRM-CM5, CSIRO-Mk3.6.0, FGOALS-g2, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, GISS-E2-R, HadGEM2-CC, HadGEM2-ES, INM- CM4, IPSL-CM5A-LR, IPSL-CM5A-MR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MPI-ESM-LR, MRI- CGCM3, and NorESM1-M) for RCP4.5 simulation. 4

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