Hydrological Sensitivity in W m 2 K 1 (% K 1 ) a. BCC_csm1.1*# Beijing Climate Center, China (2.49)

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1 Supplementary Table. AMIP/CMIP models used in this study. The corresponding equilibrium climate sensitivity (ECS) and the hydrological sensitivity based on the temperaturemediated precipitation change per unit surface warming (L v dp/dt s ) from the abrupt CO simulations are listed. The percentage of precipitation change in parentheses is relative to the multi-model-mean global-mean precipitation of 8.78 W m. Missing values are marked -. Model Modeling Center ECS in K Hydrological Sensitivity in W m K (% K ) a. BCC_csm.*# Beijing Climate Center, China.79. (.9) b. BCC_csm.m*# Beijing Climate Center, China.8. (.7) c. CCCMA_canam/esm*# Canadian Centre for Climate Modelling and Analysis,.68. (.) Canada d. CNRM_cm*# Centre National de Recherches Météorologiques,..6 (.6) France e. CSIRO_access.*# Commonwealth Scientific and Industrial Research.78.8 (.) Organization - Bureau of Meteorology, Australia f. CSIRO_access.*# Commonwealth Scientific and Industrial Research..7 (.) Organization - Bureau of Meteorology, Australia g. CSIRO_mk.6* Commonwealth Scientific and Industrial Research. - Organization - Queensland Climate Change Centre of Excellence, Australia h. GFDL_cm*# Geophysical Fluid Dynamics Laboratory, USA.78. (.8) i. GFDL_esmg*# Geophysical Fluid Dynamics Laboratory, USA..87 (.8) (no AMIP simulation results) j. GISS_er*# Goddard Institute for Space Studies, USA.7.67 (.) k. INM_cm*# Institute for Numerical Mathematics, Russia.97. (.7) l. IPSL_cma-lr*# Institut Pierre Simon Laplace, France.9.8 (.89) m. IPSL_cma-mr*# Institut Pierre Simon Laplace, France.. (.9) n. IPSL_cmb-lr*# Institut Pierre Simon Laplace, France.6.6 (.6) o. MIROC_esm*# Model for Interdisciplinary Research On Climate,.6. (.9) Japan p. MIROC_miroc*# Model for Interdisciplinary Research On Climate,.66. (.7) Japan q. MPI_esm-lr*# Max Planck Institute, Germany..6 (.) r. MPI_esm-mr*# Max Planck Institute, Germany.9.7 (.) s. MRI_cgcm*# Meteorological Research Institute, Japan.6.7 (.8) t. NCAR_cam* National Center for Atmospheric Research, USA. - u. NCAR_ccsm*# National Center for Atmospheric Research, USA.88. (.7) v. NCC_noresm-m*# Norwegian Climate Center (NCC), Norway.7.6 (.6) w. UKMO-hadgem-a*# UK Met Office Hadley Climate Center, UK.8.8 (.6) * AMIP and coupled historical-rcp. model simulations are analyzed. # models are available for the temperature-mediated precipitation response analysis.

2 Supplementary Table. Observations used in this study and corresponding interannual sensitivities to surface temperature. All cloud fraction and water vapor sensitivities are for tropical-means and precipitation sensitivity is for global-mean. Dataset Analysis Period Resolution References Interannual Sensitivity to Ts HadCRUT... surface temperature /99/ Morice et al. () Error! CERES EBAF.8 fluxes all-sky outgoing longwave radiation Terra MODIS high cloud fraction Collection 6 Aqua MODIS high cloud fraction Collection 6 Terra-Aqua combined MODIS high cloud fraction, Collection 6 AIRS effective high cloud fraction Version 6 Joint CloudSat/CALIPSO high cloud fraction Reference source not found. // Loeb et al. () Error! Reference source not found. 7/-6/ Baum et al. () Error! Reference source not found. 7/-6/ Baum et al. ( )Error! Reference source not found. 7/-6/ Baum et al. () Error! Reference source not found. 9// Kahn et al. () Error! Reference source not found. 6/6-6/.kmx.km Sassen et al. (8) not found. Wang et al. Error! Reference source () Error! Reference source not found. ISCCP high cloud fraction /99/.. Norris and Evan () not found. Error! Reference source Aqua AIRS-Aura MLS upper tropospheric water vapor 8// Jiang et al. () Error! Reference source not found. GPCP Precipitation /99/.. Huffman et al. (9) Error! Reference source not found. CMAP Precipitation /99/.. Xie and Arkin (997) source not found. Error! Re N/A.79±. W m K.8±.7 % K.±. % K.±.6 % K.68±. % K.88±. % K.9±.9 % K 9.8%±.8 % K.7±.9 W m K.±. W m K

3 Supplementary Figure. Correlations between the inter-model spread in precipitation sensitivity and the inter-model spread in various components of radiative flux sensitivities. The interannual rates are marked in blue and centennial rates are marked in red. All quantities are global-means.

4 Global dolr/dt s (W m K ) g (a) Interannual l h u k f m dn o p t v w j c e r q. 6 Tropical dolr/dt s (W m K ) b a correlation =.8 s Global dolr/dt s (W m K ) j i (b) Centennial k v c d l h o p q w g Tropical dolr/dt s (W m K ) s correlation =.9 t a BCC_csm. b BCC_csm.m c CCCMA_canam d CNRM_cm e CSIRO_access. f CSIRO_access. g CSIRO_mk.6 h GFDL_cm i GFDL_esmg j GISS_er k INM_cm l IPSL_cma-lr m IPSL_cma-mr n IPSL_cmb-lr o MIROC_esm p MIROC_miroc q MPI_esm-lr r MPI_esm-mr s MRI_cgcm t NCAR_cam u NCAR_ccsm v NCC_noresm-m w UKMO_hadgem-a Supplementary Figure. Inter-model spread in global-mean OLR sensitivity to surface temperature versus that in tropical-mean OLR sensitivity to surface temperature. (a) interannual (b) centennial. Each model is represented by a lowercase letter. Multi-model-means are marked in solid colored circles. The least-squares linear regression lines and correlation coefficients between the x-axis and y-axis variables are shown.

5 (a) MIROC-miroc [-,] 7 (b) MRI-cgcm [-,] (c) UKMO-hadgem-a [-,] CF above hpa (%) ISCCP simulator Max Overlap Random Overlap / Max + / Random 6 ISCCP simulator Max Overlap Random Overlap / Max + / Random ISCCP simulator Max Overlap Random Overlap / Max + / Random Weighted-Average CF anomaly (%) Year (d) MIROC-miroc, [-,] Corr=.9, slope= ISCCP simulator CF anomaly(%) Year (e) MRI-cgcm, [-,] Corr=.97, slope= Year (f) UKMO-hadgem-a, [-,] ISCCP simulator CF anomaly(%) ISCCP simulator CF anomaly(%) Corr=.9, slope=.88 Supplementary Figure. Calculating total high cloud fraction using weighted averages under maximum and random overlap assumptions. (a)-(c) The tropical averaged ( S- N) total high cloud fractions based on maximum and random overlap assumptions and the weighted averages of the high cloud fractions under the maximum and random overlap (/ maximum and / random) assumptions, compared to the ISCCP simulator high cloud fractions for three AMIP models. (d)-(f) The tropical-averaged de-seasonalized high cloud fraction anomalies based on the weighted averages from the maximum and random overlap assumptions scattered against the ISCCP simulator high cloud anomalies for the period of 99 to in the three AMIP models.

6 Supplementary Figure. Relationship between interannual high cloud fraction anomalies and local surface temperature anomalies. The color shadings are the regression coefficients in % K for high cloud fraction regressed onto local surface temperature from 99 to for AMIP model simulations and the multi-model-mean. 6

7 IPSL 6 6 MPI 86 MMM -. dp/dts (black-contour) -. - dω/dts (white-contour) -8.6 MMM.. MPI dcf/dts (color-shading) GISS NCC MOHC NCAR. GFDL MIROC... CSIRO..6 MRI - (a) Interannual Rates. IPSL 68 dp/dts (black-contour) CCCMA.6 dω/dts (white-contour) dcf/dts (color-shading) - GISS NCC MOHC 8 6 NCAR - MIROC GFDL - CSIRO MRI CCCMA (b) Centennial Rates Supplementary Figure. Spatial distributions of upper tropospheric vertical velocity at hpa, precipitation and high cloud fraction changes per unit surface warming. (a) interannual rates, represented by the regression slopes of each variable onto tropical-mean ( S- N) surface temperature. (b) centennial rates, represented by the differences between the st and th centuries for each variable normalized by tropical-mean surface temperature change. 7

8 dcf/dts (% K ) (a) Temperature mediated s f t g h n w l m e c b a q r j df ω /dts (% K ) p o correlation =.6 dolr/dts (W m K ) (b) Temperature mediated g f s t h correlation =.7 pa b o q r e w n j l c m dcf/dts (% K ) a BCC_csm. b BCC_csm.m c CCCMA_canam d CNRM_cm e CSIRO_access. f CSIRO_access. g CSIRO_mk.6 h GFDL_cm i GFDL_esmg j GISS_er k INM_cm l IPSL_cma-lr m IPSL_cma-mr n IPSL_cmb-lr o MIROC_esm p MIROC_miroc q MPI_esm-lr r MPI_esm-mr s MRI_cgcm t NCAR_cam u NCAR_ccsm v NCC_noresm-m w UKMO_hadgem-a Supplementary Figure 6. Relationships between the inter-model spreads in the tightening of Hadley ascent, the tropical high cloud fraction sensitivity, and the OLR sensitivity for the temperature-mediated rates. (a) Tropical-mean dcf/dt s scattered against the change of the tropical ascending area fraction per unit surface warming, df ω /dt s. The tropical ascending area is defined by ω < Pa s. (b) The tropical-mean dolr/dt s scattered against the tropicalmean dcf/dt s. The sensitivities are derived from the abrupt CO experiments using the linear regression method. Each model is represented by a lowercase letter. Multi-model-means are marked in solid colored circles. The least-squares linear regression lines and correlation coefficients between the x-axis and y-axis variables are shown. 8

9 df ω /dt s (% K ) - - w (a) Interannual e v u r q h correlation =.9 j m p f c o s t a dh w /dt s ( o K ) b n l g df ω /dt s (% K ) s (b) Centernnial g p u correlation =.6 t dh w /dt s ( o K ) o c j h me lrw a vf n b q a BCC_csm. b BCC_csm.m c CCCMA_canam d CNRM_cm e CSIRO_access. f CSIRO_access. g CSIRO_mk.6 h GFDL_cm i GFDL_esmg j GISS_er k INM_cm l IPSL_cma-lr m IPSL_cma-mr n IPSL_cmb-lr o MIROC_esm p MIROC_miroc q MPI_esm-lr r MPI_esm-mr s MRI_cgcm t NCAR_cam u NCAR_ccsm v NCC_noresm-m w UKMO_hadgem-a Supplementary Figure 7. Relationship between the change of the width of the ascending branch of the Hadley Circulation and the change of tropical ascending area per unit surface warming. (a) interannual and (b) centennial. The width of the ascending branch of the Hadley Circulation (H w ) is defined by the latitudinal width in degrees of the annual-mean zonalmean upward vertical velocity at hpa. The change of tropical ascending area per unit surface warming is based on monthly upward vertical velocity at hpa. Each model is represented by a lowercase letter. Multi-model-means are marked in solid colored circles. The least-squares linear regression lines and correlation coefficients between the x-axis and y-axis variables are shown. 9

10 L v dp/dts (W m K ) s s t t Global L v dp/dts correlation =.6 b v q n b f g f v q n g Centennial Wet-area L v dp wet /dts correlation =. l mr p u a l w m ur a p ew e Tropical df ω /dt s (% K ) hk h k j j i i c c d d o o a BCC_csm. b BCC_csm.m c CCCMA_canam d CNRM_cm e CSIRO_access. f CSIRO_access. g CSIRO_mk.6 h GFDL_cm i GFDL_esmg j GISS_er k INM_cm l IPSL_cma-lr m IPSL_cma-mr n IPSL_cmb-lr o MIROC_esm p MIROC_miroc q MPI_esm-lr r MPI_esm-mr s MRI_cgcm t NCAR_cam u NCAR_ccsm v NCC_noresm-m w UKMO_hadgem-a Supplementary Figure 8. Relationships between the tightening of Hadley ascent and precipitation changes. The tightening of the tropical ascending areas based on the upward velocity at hpa (df ω /dt s ) scattered against the global-mean precipitation change normalized by global-mean surface temperature increase (red) and the tropical wet-area mean precipitation change normalized by tropical-mean surface temperature increase (cyan) for models on the centennial time scale. Each model is represented by a lowercase letter. Multi-model-means are marked in solid colored circles. The least-squares linear regression lines and correlation coefficients between the x-axis and y-axis variables are shown.

11 Interannual Centennial Multi-Model Mean: 6 % K - W m K dcf/dt s dlwc/dt s dolr/dt s dolr clr /dt s dcre lw /dt s - Supplementary Figure 9. Relationships between the inter-model spreads in tropical high cloud fraction sensitivity and longwave radiative sensitivities. Colored circles mark the individual models interannual (blue) and centennial (red) sensitivities to surface temperature for tropical-mean high cloud fraction (CF), longwave radiative cooling (LWC), all-sky OLR, clearsky OLR (OLR clr ), and longwave cloud radiative effect (CRE lw ). Multi-model-means are shown in black +. The left y-axis is for high cloud fraction sensitivity and the right y-axis is for longwave radiative sensitivities. The across-model correlations between dcf/dt s and the radiative sensitivities are shown above the x-axis for interannual (blue) and centennial (red) rates.

12 ECS (K) d a f k b w e u correlation =.9 correlation =. t g s h q f r sv p n n gm ew c dcf/dt s (% K ) t h l u b oo va p j q r j a BCC_csm. b BCC_csm.m c CCCMA_canam d CNRM_cm e CSIRO_access. f CSIRO_access. g CSIRO_mk.6 h GFDL_cm i GFDL_esmg j GISS_er k INM_cm l IPSL_cma-lr m IPSL_cma-mr n IPSL_cmb-lr o MIROC_esm p MIROC_miroc q MPI_esm-lr r MPI_esm-mr s MRI_cgcm t NCAR_cam u NCAR_ccsm v NCC_noresm-m w UKMO_hadgem-a Supplementary Figure. Relationship between tropical-mean high cloud fraction sensitivity and equilibrium climate sensitivity. ECS scattered against tropical-mean dcf/dt s (interannual in blue, centennial in red) across the models. Each model is represented by a lowercase letter. Multi-model-means are marked in solid colored circles. The least-squares linear regression lines and correlation coefficients between the x-axis and y-axis variables are shown.

13 dcf ISCCP (%) dcf Terra_MODIS (%) Terra MODIS Slope =.8 +/.7 %/K Correlation =.6 (a) dt s (K) ISCCP Slope =.9 +/.9 %/K Correlation =.8 (d) dt s (K) dcf Aqua_MODIS (%) dcf AIRS (%) Aqua MODIS Slope =. +/. %/K Correlation =.8 (b) dt s (K) Aqua AIRS Slope =.68 +/. %/K Correlation =. (e) dt s (K) dcf Aqua+Terra_MODIS (%) dcf CloudSat-CALIPSO (%) Terra+Aqua MODIS Slope =. +/.6 %/K Correlation =.6 (c) dt s (K) CloudSat+CALIPSO Correlation =.8 Slope =.88 +/. %/K (f) dt s (K) Supplementary Figure. Observed tropical-mean high cloud fraction sensitivity to surface temperature. (a) Terra MODIS (7/-6/), (b) Aqua MODIS (7/-6/), (c) Terra-Aqua MOSIS (7/-6/), (d) ISCCP (/99/), (e) Aqua AIRS (9/- /), and (f) CloudSat/CALIPSO joint retrieval (6/6-6/). The least-squares linear regression lines are drawn, with correlations and regressions slopes marked.

14 CCCMA_canam CNRM_cm CSIRO_mk.6 (a) Correlation =.86 Slope = 9.7 +/-.99 %/K (b) Correlation =.77 Slope = 9.9 +/. %/K (c) Correlation =.89 Slope =.79 +/.6 %/K GFDL_cm GISS_e-r INM_cm (d) Correlation =.8 Slope =. +/.6 %/K (e) Correlation =.9 Slope =.8 +/-.76 %/K (f) Correlation =.89 Slope =. +/-.98 %/K IPSL_cma-lr MIROC_miroc MPI_esm-lr (g) Correlation =.9 Slope =.8 +/-.9 %/K (h) Correlation =.8 Slope =. +/.6 %/K (i) Correlation =.8 Slope =.8 +/.6 %/K MRI_cgcm NCAR_cam NCC_noresm-m (j) Correlation =.86 Slope =.8 +/. %/K UKMO_hadgem-a (k) Correlation =.87 Slope =.8 +/. %/K Observations (l) Correlation =.86 Slope =.9 +/. %/K dt s (K) (m) Correlation =.86 Slope =. +/. %/K dt s (K) (n) Correlation =.7 Slope = 9.8 +/.8 %/K dt s (K) Supplementary Figure. Upper tropospheric ( hpa to hpa) water vapor path sensitivity to surface temperature in models and observations. Aqua AIRS and Aura MLS combined water vapor profiles are used for the observed sensitivity. The least-squares linear regression lines are drawn, with correlations and regressions slopes marked.

15 Centennial global L v dp/dts (W m K ) correlation =. c o q p f e h ta w Interannual global L v dp/dts (W m K ) m r n v d k l u b OBS. s a BCC_csm. b BCC_csm.m c CCCMA_canam d CNRM_cm e CSIRO_access. f CSIRO_access. g CSIRO_mk.6 h GFDL_cm i GFDL_esmg j GISS_er k INM_cm l IPSL_cma-lr m IPSL_cma-mr n IPSL_cmb-lr o MIROC_esm p MIROC_miroc q MPI_esm-lr r MPI_esm-mr s MRI_cgcm t NCAR_cam u NCAR_ccsm v NCC_noresm-m w UKMO_hadgem-a Supplementary Figure. Relationship between interannual and centennial global-mean precipitation sensitivity to surface temperature. Centennial L v dp/dt s scattered against interannual L v dp/dt s. Each model is represented by a lowercase letter. The observation-based interannual L v dp/dt s with the 9% confidence level is marked in gray shading. The ensemble model means for the models and the five better-performing models are shown in solid circles and black cross, respectively. The least-squares linear regression line and correlation coefficient between the x-axis and y-axis variables are shown.

16 (a) GPCP (b) CMAP L v dp (W m ) L v dp (W m ) - Correlation =.6 Slope =.7 +/ dt s (K) - Correlation =.9 Slope = -. +/ dt s (K) Supplementary Figure. Observed global-mean precipitation sensitivity to surface temperature. (a) GPCP and (b) CMAP global-mean precipitation anomalies scattered against surface temperature anomalies for the period of 99-. The least-squares linear regression lines are drawn, with correlations and regressions slopes marked. The CMAP precipitation sensitivity is not used due to large uncertainty. 6

17 Temperature Mediated dolr/dt s (W m K ) g op q j m l ht r e c (a) 6 Interannual dolr/dt s (W m K ) f wn correlation =. ab s Centennial dolr/dt s (W m K ) (b) correlation =.9 l p f n ob ew gh m q r c a Temperature Mediated dolr/dt s (W m K ) t s a BCC_csm. b BCC_csm.m c CCCMA_canam d CNRM_cm e CSIRO_access. f CSIRO_access. g CSIRO_mk.6 h GFDL_cm i GFDL_esmg j GISS_er k INM_cm l IPSL_cma-lr m IPSL_cma-mr n IPSL_cmb-lr o MIROC_esm p MIROC_miroc q MPI_esm-lr r MPI_esm-mr s MRI_cgcm t NCAR_cam u NCAR_ccsm v NCC_noresm-m w UKMO_hadgem-a Supplementary Figure. Relationships between interannual, temperature-mediated and centennial tropical-mean dolr/dt s. (a) The temperature-mediated dolr/dt s scattered against the interannual dolr/dt s. (b) The centennial dolr/dt s scattered against the temperaturemediated dolr/dt s. Multi-model-means are marked in solid circles. The least-squares linear regression lines and correlation coefficient between the x-axis and y-axis variables are shown. 7

18 Supplementary Discussion The dominance of longwave radiative control on precipitation sensitivity. The dominance of longwave radiation in controlling the global-mean precipitation change is remarkable. We find that the inter-model spread in global-mean dp/dt s is highly correlated with the model differences in global-mean dlwc/dt s (R =.9 for interannual and.8 for centennial rates, Supplementary Figure ) and the latter is mainly driven by the outgoing longwave radiation (OLR) sensitivity (dolr/dt s ) at the top-of-atmosphere (TOA) (Supplementary Figure ). Although the atmospheric column LWC is strongly enhanced through increased net downward surface longwave radiation (SLR = surface downward upward longwave flux) when the surface warms, the model diversity in dlwc/dt s is not dominated by the differences in dslr/dt s (Supplementary Figure ). Treating OLR as the sum of clear-sky OLR (OLR clr ) and longwave cloud radiative effect (CRE lw ), we find that the model spreads in dolr clr /dt s and dcre lw /dt s are both correlated with that in dp/dt s with larger correlations between dcre lw /dt s and dp/dt s (R=.7 for interannual and.77 for centennial rates, Supplementary Figure ). Thus, it is reasonable to hypothesize that clouds play an important role in causing the model differences in dolr/dt s and therefore dlwc/dt s, which governs the inter-model spread in dp/dt s. Model spreads in tropical-mean high cloud fraction sensitivity and longwave radiative sensitivities. Supplementary Figure 9 shows the simulated high cloud fraction sensitivity (dcf/dt s ) in relation to various components of the longwave radiative sensitivities on interannual (blue) and centennial (red) time scales. The rates of dcf/dt s vary from.9% K to.% K on the interannual time scale for the AMIP models and from.87% K to.6% K for the coupled models on the centennial time scale (CNRM_cm and INM_cm models are missing cloud fraction outputs). The multi-model-means are.7% K for interannual and 8

19 .% K for centennial time scales. The inter-model spread in dcf/dt s is highly correlated with that of dlwc/dt s on both time scales (correlation R =.6 and., respectively), but not with dswa/dt s (R =. and. respectively, figure not shown). The correlations between dcf/dt s and dlwc/dt s largely result from the role of high cloud fraction in regulating dolr/dt s. In comparison, the correlations between dcf/dt s and dslr/dt s are less than. at both time scales (figure not shown). The inter-model spread in dcf/dt s is correlated with both model spreads in dolr clr /dt s (R =.6 for interannual and R =.9 for centennial) and longwave component of cloud radiative effect (dcre lw /dt s ) (R =.6 for both time scales), suggesting that the amount of high clouds governs not only longwave CRE, but also modulates the capacity of clear-sky LWC by changing the upper tropospheric dry and clear areas. High cloud fraction sensitivity in relation to ECS. We find that the correlations between dcf/dt s and equilibrium climate sensitivity (ECS) are not statistically significant, R =.9 for the models on the interannual and R =. for the available models on the centennial time scales (Supplementary Figure ). This indicates that high cloud fraction sensitivity is not a dominant factor in driving the model discrepancy in ECS. Contribution of upper tropospheric water vapor to the longwave radiative feedback biases. We have analyzed the simulated upper tropospheric water vapor sensitivity to surface warming in the models and find all models capture the increase of upper tropospheric water vapor path (UTWVP) with surface temperature, at the rates between 9.% K and.6% K, approximately consistent with the Clausius-Clapeyron relation. The multi-model-mean is.9% K, higher than that derived from the combined AIRS and MLS water vapor observations, 9.6% ±.% K (Supplementary Figure ), although within the AIRS and MLS data uncertainty of ~%. All models (except CNRM_cm) produce greater upper tropospheric moistening with 9

20 surface warming than the observations. The CNRM_cm model has the largest decrease of high cloud fraction with surface warming (Figure a) and also the weakest upper tropospheric moistening (Supplementary Figure b). The moist biases in the rest of the models, consistent with relatively weak decreases of high cloud fraction (the correlation between the spreads in dutwvp/dt s and dcf/dt s for the models is.7), would contribute to the low biases in the magnitudes of dolr/dt s. However, based on the calculations using the radiative kernels Error! Reference source not found.,error! Reference source not found., we find that the ensemble-mean moist bias of % K in the upper troposphere would only contribute to a small fraction of the low bias in dolr/dt s, on the order of. W/m K. In addition, the inter-model spread in dutwvp/dt s has rather weak correlations with the spreads in dolr/dt s, dolr clr /dt s and dcre lw /dt s (R =.,.,.6, respectively) on the interannual time scale. Therefore, we conclude that the misrepresentation of dcf/dt s is a dominant source for the model spread in dolr/dt s across the CMIP models and the moist bias in the upper troposphere associated with the muted high cloud shrinkage contributes only slightly to the low bias in the magnitude of dolr/dt s. Determination of the observation-based interannual precipitation sensitivity. Two sets of observations are used to determine the best estimate of the interannual precipitation sensitivity. First, we obtain an OLR-constrained L v dp/dt s based on the approximately linear relationship between the model simulated interannual tropical-mean dolr/dt s and global-mean L v dp/dt s and the CERES observed tropical-mean dolr/dt s, i.e., the OLR-constrained L v dp/dt s = A (dolr/dt s ) CERES + B + ε, where A and B are the slope and intercept for the least squares regression across the models, respectively, and ε is the linear fitting residual. The statistical distributions of the slope and intercept for the regression between the modeled dolr/dt s and

21 L v dp/dt s are determined by bootstrap iterations with replacement. The resulting mean slope and intercept are. and.6, respectively. With the CERES dolr/dt s at.8±. W m K, the mean value of the OLR-constrained L v dp/dt s = =.8 W m K with the standard deviation of. W m K. Assuming that L v dp/dt s contains random variations not captured by the linear relation with dolr/dt s, the statistics of the fitting residual ε is characterized by all the models fitting residuals, which yield a standard deviation of.9. Thus, the OLR-constrained L v dp/dt s has a mean of.8 W m K and a standard deviation of. =.9 +. W m K. Hence, the value of the OLR-constrained L v dp/dt s at the 9% confidence level (within two times of the standard deviation) is.8 ±.8 W m K. Second, we compute the interannual precipitation sensitivity directly from the least squares regression of the GPCP precipitation onto the HadCRUT surface temperature for the period of 99 to. The -month running averaging is applied onto the de-seasonalized anomalies. This gives the GPCP L v dp/dt s at.7±.9 W m K. Third, we choose the overlapped range of the two observational measures of the interannual L v dp/dt s,.8-.6 W m K as the best estimate of the observation-based short-term precipitation sensitivity at the 9% confidence level (Figure ). Significance tests for the correlations in the study. For correlation coefficients involving models, the -sided student-t test requires R. for the 9% significance level and R.8 for the 99% significance level. Hence, all the correlations relevant to our conclusions are statistically significant at the 9% level and in many cases at 99% significance level.

22 Supplementary References. Soden, Brian J., Isaac M. Held, Robert Colman, Karen M. Shell, Jeffrey T. Kiehl, Christine A. Shields, Quantifying climate feedbacks using radiative kernels, J. Clim.,, (8).. Shell, Karen M., Jeffrey T. Kiehl, and Christine A. Shields, Using the radiative kernel technique to calculate climate feedbacks in NCAR s Community Atmospheric Model, J. Clim.,, 69-8 (8).. Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT dataset, J. Geophys. Res., 7, D8, doi:.9/jd787 ().. Loeb, N. G., S. Kato, W. Su, T. Wong, F. G. Rose, D. R. Doelling, J. R. Norris, and X. Huang, Advances in understanding top-of-atmosphere radiation variability from satellite observations. Surv. Geophys.,, 9-8. DOI.7/s7--97 ().. Baum, B. A., W. P. Menzel, R. A. Frey, D. C. Tobin, R. E. Holz, S. A. Ackerman, A. K. Heidinger, and P. Yang, MODIS Cloud-Top Property Refinements for Collection 6. J. Appl. Meteor. Climatol.,, 6. doi: (). 6. Kahn B. H. et al, The atmospheric infrared sounder version 6 cloud products. Atmos. Chem. Phys. :99 6. doi:.9/acp-99- (). 7. Sassen, K., Z. Wang, and D. Liu, 8: The global distribution of cirrus clouds from CloudSat/CALIPSO measurements, J. Geophys. Res.,, DA, doi:.9/8jd997 (8).

23 8. Wang, Z., D. Vane, G. Stephens, and D. Reinke, Level combined radar and lidar cloud scenario classification product process description and interface control document. JPL Rep., pp. (). 9. Norris J. R., and A. T. Evan, Empirical removal of artifacts from the ISCCP and PATMOS-x satellite cloud records. J. Atmos. Ocean. Technol. :69 7. doi:.7/jtech-d8. ().. Jiang, J. H., et al. (), Evaluation of cloud and water vapor simulations in CMIP climate models using NASA A-Train satellite observations, J. Geophys. Res., 7, D, doi:.9/jd77 ().. Huffman, G.J, R.F. Adler, D.T. Bolvin, G. Gu, Improving the Global Precipitation Record: GPCP Version.. Geophys. Res. Lett., 6,L788, doi:.9/9gl (9).. Xie, P., and P.A. Arkin, Global precipitation: A 7-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 9 8 (997).