## 2 Materials and Methods
### Climate Data and Downscaling Climate Variables  
The daily rainfall, solar radiation, maximum temperature, and minimum temperature of two meteorological stations and six rain gauge stations, distributed in the Northwest Selangor, were obtained from Hydrology Division and Malaysian Meteorological Department (MMD). Daily climate variables for 10 Global Climate Models (GCMs) were downloaded from CMIP5 (Coupled Model Intercomparison Project, [[https://esgf-node.llnl.gov/search/cmip5/](https://esgf-node.llnl.gov/search/cmip5/)]([https://esgf-node.llnl.gov/search/cmip5/](https://esgf-node.llnl.gov/search/cmip5/)), accessed on 21 October 2021) for the base period of 1976-2005 and future period of 2010-2099. The climate variables were then downscaled using an interactive Climate-Smart Decision Support System (CSDSS) tool [35], which comprises an ensemble of 10 GCMs as shown in Figure 3. To simulate GCM scenarios at the local scale, statistical downscaling methods have been introduced by utilising statistical relationships between model output climate variables and historical climate variables. The relationship between the coarse-scale patterns and the local weather must remain stationary during downscaling. The CSDSS graphical user-interface simulates climate data instantly on daily and monthly time-series, and displays the data in tables and graphs.  
Figure 1: Location of the experimental plot in the Northwest Selangor Irrigation Scheme in Malaysia. (**a**) Rice production in large-scale granaries (Agrofood Statistics, MOA 2016) & (**b**) Study location.  
Figure 3: _Cont._  
Figure 2: A view of different equipment installed in a paddy plot. (**a**) Layout for installed equipment. (**b**) Experimental paddy plot. (1. Inlet-RBC Parshall Flume, 2. Rain-gauge, 3. Marriott tube, 4. Micro-lysimeter, 5. e-Water level sensor, 6. ETgauge, and 7. Field outlet).  
functions and data visualisations are available in it. MATLAB programming codes were written for the GCMs data under the three RCP scenarios (RCP4.5, RCP6.0, and RCP8.5). New climate scenarios and/or GCMs can be easily added or updated to the MATLAB database through an independent executable module and the large amount of GCMs data stored in the database. Figure 3b illustrates the steps followed in simulating daily climate sequences. The CSDSS can be used in different Meteorological stations/regions/basins by altering CMIP5 ([[https://esgf-node.llnl.gov/search/cmip5/](https://esgf-node.llnl.gov/search/cmip5/)]([https://esgf-node.llnl.gov/search/cmip5/](https://esgf-node.llnl.gov/search/cmip5/)))/CMIP6 ([[https://esgf-node.llnl.gov/search/cmip6/](https://esgf-node.llnl.gov/search/cmip6/)]([https://esgf-node.llnl.gov/search/cmip6/](https://esgf-node.llnl.gov/search/cmip6/)), accessed on 21 October 2021) GCMs data. To run downscaling model, CSDSS requires observed climate data for a particular location/station. CSDSS was successfully used to downscale climate variables (Tmax, Tmin, rainfall, relative humidity, wind speed, solar radiation, and potential evapotranspiration) and simulate the hydrologic response of the Kurau River Basin for determining inflow patterns of the Bukit Merah Reservoir ([[https://doi.org/10.1038/s41598-020-65114-w](https://doi.org/10.1038/s41598-020-65114-w)]([https://doi.org/10.1038/s41598-020-65114-w](https://doi.org/10.1038/s41598-020-65114-w)), accessed on 21 October 2021).