This paper examines the projected changes in rainfall in Southeast Asia (SEA) in the twenty-first century based on the multimodel simulations of the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment-Southeast Asia (SEACLID/CORDEX-SEA). A total of 11 General Circulation Models (GCMs) have been downscaled using 7 Regional Climate Models (RCMs) to a resolution of 25 km × 25 km over the SEA domain (89.5° E-146.5° E, 14.8° S-27.0° N) for two different representative concentration pathways (RCP) scenarios, RCP4.5 and RCP8.5. The 1976-2005 period is considered as the historical period for evaluating the changes in seasonal precipitation of December-January-February (DJF) and June-July-August (JJA) over future periods of the early (2011-2040), mid (2041-2070) and late twenty-first century (2071-2099). The ensemble mean shows a good reproduction of the SEA climatological mean spatial precipitation pattern with systematic wet biases, which originated largely from simulations using the RegCM4 model. Increases in mean rainfall (10-20%) are projected throughout the twenty-first century over Indochina and eastern Philippines during DJF while a drying tendency prevails over the Maritime Continent. For JJA, projections of both RCPs indicate reductions in mean rainfall (10-30%) over the Maritime Continent, particularly over the Indonesian region by mid and late twenty-first century. However, examination of individual member responses shows prominent inter-model variations, reflecting uncertainty in the projections.

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Potential impacts of climate change on the streamflow of the Bernam River Basin in Malaysia are assessed using ten Global Climate Models (GCMs) under three Representative Concentration Pathways (RCP4.5, RCP6.0 and RCP8.5). A graphical user interface was developed that integrates all of the common procedures of assessing climate change impacts, to generate high resolution climate variables (e.g., rainfall, temperature, etc.) at the local scale from large-scale climate models. These are linked in one executable module to generate future climate sequences that can be used as inputs to various models, including hydrological and crop models. The generated outputs were used as inputs to the SWAT hydrological model to simulate the hydrological processes. The evaluation results indicated that the model performed well for the watershed with a monthly R 2 , Nash-Sutcliffe Efficiency (NSE) and Percent Bias (PBIAS) values of 0.67, 0.62 and −9.4 and 0.62, 0.61 and −4.2 for the calibration and validation periods, respectively. The multi-model projections show an increase in future temperature (t max and t min ) in all respective scenarios, up to an average of 2.5 • C for under the worst-case scenario (RC8.5). Rainfall is also predicted to change with clear variations between the dry and wet season. Streamflow projections also followed rainfall pattern to a great extent with a distinct change between the dry and wet season possibly due to the increase in evapotranspiration in the watershed. In principle, the interface can be customized for the application to other watersheds by incorporating GCMs' baseline data and their corresponding future data for those particular stations in the new watershed. Methodological limitations of the study are also discussed.

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Agro-hydrological water management frameworks help to integrate expected planned management and expedite regulation of water allocation for agricultural production. Low production is not only due to the variability of available water during crop growing seasons, but also poor water management decisions. The Tanjung Karang Rice Irrigation Scheme in Malaysia has yet to model agro-hydrological systems for effective water distribution under climate change impacts. A climate-smart agro-hydrological model was developed using Excel-based Visual Basic for Applications (VBA) for adaptive irrigation and wise water resource management towards water security under new climate change realities. Daily climate variables for baseline (1976–2005) and future (2010–2099) periods were extracted from 10 global climate models (GCMs) under three Representative Concentration Pathway scenarios (RCP4.5, RCP6.0, and RCP8.5). The projected available water for supply to the scheme would noticeably decrease during the dry season. The water demand in the scheme will differ greatly during the months in future dry seasons, and the increase in effective rainfall during the wet season will compensate for the high dry season water demand. No irrigation will therefore be needed in the months of May and June. In order to improve water distribution, simulated flows from the model could be incorporated with appropriate cropping patterns.