Description of MRCM

MRCM26 used in the study is based on the Abdus Salam International Centre for Theoretical Physics Regional Climate Model Version 3 (RegCM3)31 but with several improvements32,33,34,35, achieved through incorporation of new physical schemes or modification of original schemes MRCM has been rigorously tested against observations, in its ability to simulate key observed climate features, across several regions (e.g., North America32, West Africa36, Southwest Asia16, South Asia17, Maritime Continent37). In particular, previous studies38,39 tested extensively the performance of the irrigation module used within MRCM. Hence, we use MRCM, including the irrigation module, to simulate climate over China, a region which probably has the largest irrigated area in the world40. Irrigation is simulated in the model, by replenishing the root-zone soil moisture to field capacity at the beginning of each month, whenever needed during summer (May to September), and wherever the grid point is equipped for irrigation according to the Historical Irrigation Data Set40.

Simulation of the summer climate of North China Plain (NCP)

Before making climate projections, we analyze simulations by the MRCM constrained by boundary conditions from reanalysis data and evaluate its performance against observations. The rationale for carrying these experiments is to test the model skill in reproducing observed regional climate, as well as to study the impact of land use change on the historical climate of the region. As irrigation is widely practiced in NCP (Fig. 1b), it is essential that we evaluate the impact of irrigation on surface climate. In this regard, additional 30 years (1982–2011) numerical experiments consisting of control (CONT, without irrigation module) and irrigation (IRR, with irrigation module) simulations are performed using MRCM (Supplementary Table 3). The MRCM experiments adopt the same model configuration (e.g., domain coverage, spatiotemporal resolution, and physical parameters) as one used in the GCM downscaling, but are driven by the 1.5° × 1.5° 6-hourly ERA-Interim data41 as lateral boundary conditions and the 1° × 1° weekly NOAA optimum interpolation sea surface temperatures for the ocean surfaces42. To evaluate the two simulations, we compare the simulated surface conditions to the Climate Research Unit (CRU)43 data, focusing on several key surface variables such as surface temperature (Supplementary Figs. 5 and 6), specific humidity (Supplementary Figs. 7 and 8), and TW (Supplementary Figs. 9 and 10). The finger print of irrigation can be seen clearly by comparing field observations of surface conditions to the results of the numerical model simulations. Without explicit representation of irrigation and its direct and indirect effects, models would consistently simulate a summer climate over the NCP that features warmer temperature, drier air, and cooler TW. These difference between simulations and observations are consistent with the finger print of irrigation. Indeed, inclusion of a reasonable representation of irrigation into the model improves the correspondence between the model results and observations in terms of surface temperature, atmospheric humidity, and surface TW, leading to statistically significant reductions of overall biases. In a recent coordinated set of numerical models’ simulations over Eastern China, a warm and dry bias in surface conditions was identified as a common deficiency in the ability of other regional climate models (without irrigation) to simulate the observed climate of this region44.

Regional climate change projections

The model domain covers Eastern China including NCP, which is centered at 115°E and 31.5°N with a 25 km grid spacing on a Lambert conformal projection (Supplementary Fig. 1). The atmospheric lateral boundary conditions for MRCM are specified based on GCM simulations, selected from among CMIP5 27 participant models. Since the regional climate model is constrained by atmospheric lateral boundary conditions, selection of the GCMs is important. In this study, three GCMs are carefully selected based on a screening process, including rigorous evaluation of the GCM performance in simulating key climate variables for the historical period over the target domain (see selection of GCMs section below). The selected models are the Community Climate System Model Version 4 (hereafter referred to as CCSM)45, Australian Community Climate and Earth System Simulator Version 1.0 (hereafter referred to as ACCESS)46, and Max-Planck-Institution Earth System Model running on Medium Resolution grid (hereafter referred to as MPI)47. For each selected GCM, two historical climate simulations are performed assuming historical GHG concentrations, with and without irrigation module (HIST), for the period 1975–2005. To quantify the impact of a range of GHG concentrations, four future projection simulations, with and without irrigation module, are performed with two different RCP scenarios28, namely, RCP4.5 and RCP8.5 for the period 2070–2100 (Supplementary Table 2). RCP8.5 is a rising concentration pathway leading to 8.5 W m−2 of radiative forcing by 2100 and can be considered a BAU scenario. RCP4.5 is a stabilization scenario after about 2060, leading to 4.5 W m−2 of radiative forcing by 2100. It represents moderate mitigation effort. In total, six sets of experiments are performed with MRCM over Eastern China. Each set of experiments consists of three ensemble members forced by three GCMs. The historical baseline period consists of 31 years from 1975 to 2005 and the projected future period covers 31 years from 2070 to 2100.

Selection of the GCMs

The GCMs used for specifying the boundary conditions for MRCM are selected from among the many participant models in CMIP5 by applying the following criteria:

We adopt the 19 GCMs evaluated favorably by McSweeney et al.48 based on their performance over Southeast Asia. And then we select 6 GCMs out of the 19 by requiring an oceanic horizontal resolution of 1.11° or higher, capable of simulating complex ocean processes over this region. Over land, the surface temperature, TW, relative humidity, and precipitation from GCMs are objectively analyzed and compared to CRU43, ERA-Interim41, and TRMM49 datasets. To assess the performance of each GCM, the NRMSE, PCC, and annual cycle for each variable are compared separately over two regions (Northeastern China and Southeastern China).

As a result of applying the above criteria, three GCMs are selected: CCSM445, ACCESS1.0 46, and MPI-ESM-MR47. More detailed information about the GCMs is presented in Supplementary Table 1.

Bias correction

Simulations by a regional climate model may contain a systematic bias arising from inadequate physics, and/or bias in the global climate model simulations used as lateral boundary conditions50,51. These model biases impact historical as well as future climate change projections. To reduce the impact of this bias, we applied the same bias correction procedure developed by Pal and Eltahir16 for correcting future projections in southwest Asia. This methodology allows correction of daily TW max . TW is computed by the formulation developed by Davis-Jones52. Reliable reanalysis data at high spatial and temporal resolution is the key to correct bias in simulated daily variables. ERA-Interim reanalysis represents spatially complete and dynamically consistent estimates of the state of the climate system41 and is therefore used for the following bias correction procedure. In the first step, TW is computed by the formulation developed by Davies-Jones52for both the MRCM hourly output and the ERA-Interim reanalysis 3-hourly 0.75° × 0.75° data. In this stage, the 6-h running average (6-h window) is calculated and then its daily maximum (denoted by TW max ) is selected for each day, and then the ERA-Interim TW max data are transferred from the 0.75° × 0.75° horizontal grid to the 25-km MRCM grid. In the second step, consistent MRCM and ERA-Interim climatologies of TW max is computed for each day of the year on the MRCM 25-km grid. In the final step, the magnitude of the bias for each day of the year is estimated by the difference between 30-day running means of the two climatologies. The daily bias is then applied to the MRCM daily values of TW max for the present-day and future climates.

Code availability

Code from this study is available from the corresponding author upon reasonable request.

Data availability

The Global 30 Arc-Second Elevation (GTOPO30) used in Fig. 1a is from International Centre for Theoretical Physics (ICTP) data server (http://clima-dods.ictp.it/data/regcm4/SURFACE). The area equipped for irrigation (AEI) used in Fig. 1b is from Historical Irrigation Data (https://mygeohub.org/publications/8/2). The annual precipitation used in Fig. 1b is from Tropical Rainfall Measuring Mission (TRMM, https://trmm.gsfc.nasa.gov/). The 3-hourly temperature and dew point temperature to calculate daily TW max used in Fig. 1c are from the ERA-Interim reanalysis (http://apps.ecmwf.int/datasets/). The population density used in Fig. 1d is from Global Rural–Urban Mapping Project (GRUMP, http://sedac.ciesin.columbia.edu/data/set/grump-v1-population-density).

All MIT Regional Climate Model results used for the present study are available from the authors on reasonable request.