Projected changes in temperature and precipitation over mainland Southeast Asia by CMIP6 models

Five mainland SEA countries (Cambodia, Laos, Myanmar, Vietnam, and Thailand) are threatened by climate change. Here, the latest 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) is employed to examine future climate change in this region under two SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios (SSP2-4.5 and SSP5-8.5). The bias-corrected multi-model ensemble (MME) projects a warming (wetting) over Cambodia, Laos, Myanmar, Vietnam, and Thailand by 1.88 – 3.89, 2.04 – 4.22, 1.88 – 4.09, 2.03 – 4.25, and 1.90 – 3.96 (cid:1) C (8.76 – 20.47, 12.69 – 21.10, 9.54 – 21.10, 13.47 – 22.12, and 7.03 – 15.17%) in the 21st century with larger values found under SSP5-8.5 than SSP2-4.5. The MME model displays approximately triple the current rainfall during the boreal summer. Overall, there are robust increases in rainfall during the Southwest Monsoon (3.41 – 3.44, 8.44 – 9.53, and 10.89 – 17.59%) and the Northeast Monsoon ( (cid:3) 2.58 to 0.78, (cid:3) 0.43 to 2.81, and 2.32 to 5.45%). The effectiveness of anticipated climate change mitigation and adaptation strategies under SSP2-4.5 results in slowing down the warming trends and decreasing precipitation trends after 2050. All these ﬁ ndings imply that member countries of mainland SEA need to prepare for appropriate adaptation measures in response to the changing climate. cant differences. However, there are signi ﬁ cant precipitation changes.


INTRODUCTION
A large number of climate extremes (e.g., floods, droughts, heat waves, and extreme precipitation) have been recently observed across the globe in conjunction with global warming. Several studies reveal that continued greenhouse gas emissions will lead to long-term changes in the climate In present-day climate simulations, general circulation models (GCMs) are the major tools used to project future climate based on well-established physical principles (Randall et al. ). Both GCMs and observations on the global (Pall et al. ) and regional scales (Ghosh et al. ) have confirmed that rising extreme precipitation events are linked to climate warming, which leads to increased atmospheric moisture content and specific humidity (Willett et al. ).
Extreme precipitation is projected to intensify in the future under a warming climate (Ali & Mishra ).
To better understand past, present, and future climate Projected changes in temperature and precipitation extremes are generally more pronounced in CMIP5 than in CMIP3 (Sillmann et al. a, b). Previous studies of climate model comparison revealed that CMIP5 models perform better than CMIP3 models, particularly over North and Cen-  (Alexander & Arblaster ). Most GCMs represent climatic variation at gross spatial resolutions (typically 100-300 km) which are not capable in impact assessments that require relatively fine spatial resolutions of just a few kilometers.
The latest CMIP6 models use the combination of shared socioeconomic pathways (SSPs) and representative concentration pathways (RCPs) that make more reasonable for future scenarios (Eyring et al. ). As CMIP6 has been improved in several aspects (e.g., higher horizontal resolution, better representation of synoptic processes, and better agreement with the estimation of global energy balance), more reasonable results can be obtained from  In this study, we analyzes the changes in mean temperature and precipitation using the latest Couple Model Intercomparison Project Phase 6 (CMIP6) model simulation dataset over mainland SEA. It is still not well understood how the new CMIP6 models can effectively simulate the climate response to anthropogenic forcing in this region.
The overall aim of this study is to reveal the ability of the CMIP6 model to simulate the climate response to anthropogenic forcing over five mainland Southeast Asian countries.
The specific questions to address are: what are the long-term observed trends in temperature and precipitation over each country and how are they likely to change in the seasonal monsoon for the near-, mid-, and far-future periods? This is an initial step required for a decision on what appropriate level of adaptation measures to the impacts of projected climate-extreme events.

Study region
Our region of interest consists of the five mainland SEA countries: Cambodia, Laos, Myanmar, Vietnam, and Thailand (CLMVT), as displayed in Figure 1. We first examined the historical climate over the full domain of SEA similar to . Then, we projected the change in climate and seasonal monsoon precipitation over each country. SEA experiences two distinct sub-monsoon seasons: wet and dry. The same weather system that delivers rain during India's monsoon season also affects Southeast Asia, but at different times (Kripalani et al. ).

Model data
We examined 18 CMIP6 models (available since February 2020 when we started this work) from the CMIP6 database website (https://esgf-node.llnl.gov/search/cmip6), as given in Table 1  SSP-RCP scenarios, i.e., medium-emission (SSP2-4.5) and high-emission (SSP5-8.5). For a fair comparison, all models were regridded to 0.25 × 0.25 resolution using a bilinear interpolation technique similar to the observation datasets.

Bias correction method
Bias correction is widely used in climate impact modeling.
The aim is to adjust selected statistics (mean, variance, and/or quantile) in a climate model simulation to better match observed statistics during a reference period. Many bias correction methods have been employed in previous studies (Teutschbein & Seibert ; Supharatid ). In this study, we employed a 'variance scaling' method to correct the historical and projected temperature over SEA from CMIP6 models. This approach can guarantee that the adjusted model simulation in the reference period has the same mean and standard deviation as the observations. The first step we used 'Delta change' approach to adjust the temperature at each grid point (T i,j (d)) as given in The second step is to find the corresponding anomalies (T = i,j ) in the reference and projection periods by Then, the anomalies from Equations (2) and (3) are scaled by the ratio of their observed (σ obs, i,j ) and reference Finally, the corrected-adjust values during the reference and projection periods can be found: In addition, we implement the 'Empirical quantile mapping (EQM)' method to remove the systematic precipitation biases in the GCMs simulation. The EQM, corrects the distribution shape of the monthly precipitation based on cumulative density functions (CDFs), is constructed for both the observed and the GCM simulation (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) for all months. For a given monthly precipitation, the cumulative density function of a control simulation is first matched with the CDF of the observations, generating a correction function depending on the quantile. Then, this correction function is used to unbias them from the climate simulation quantile by quantile. Finally, the monthly precipitation for the reference and future periods are obtained by Equations (8) and (9).
where F is the cumulative distribution function (CDFs) and F À1 is its inverse.

ANALYSIS OF THE PRESENT CLIMATE
Model simulation in the reference period We first examined the CMIP6 models performance over  range 0.8-0.9, and show larger spread in SD than R and RMSD. After bias correction (green and pink symbols), R increases (>0.9) and RMSD and SD are significantly lower than their values before bias correction. Overall, the CMIP6 model has reliable capabilities in simulating the annual-mean temperature. For precipitation (Figure 6(b)), all observation datasets were found to give high R (>0.9) but lower than those of temperature. We observe distinctly

PROJECTED CHANGES IN ANNUAL-MEAN TEMPERATURE AND PRECIPITATION
The spatial distribution of the projected change in T mean and P mean under two different SSP-RCP scenarios is shown in Figure 7. The projected annual-mean temperature (Figure 7(a)) under two scenarios increases with time and shows little local difference in its pattern. However, it shows a larger increase over northern CLMVT, with the largest increase over northern Vietnam, Laos, and Myanmar.
In the near-future, the annual-mean temperature averaged over CLMVT is projected to increase by 0.63 and 0.77 C under SSP2-4.5 and SSP5-8.5, respectively. In the midfuture, it is projected to increase by 1.28 and 1.88 C, respectively, and in the far-future, it is projected to increase by 1.80 and 3.36 C, respectively.  In contrast to T mean , the changes in annual-mean precipitation show significant regional differences (Figure 7(b)).
Projection of the NEMR does not fit well the Gamma pdf as compared to the southwest Monsoon rainfall (SWMR). The pdf curve during the wet season becomes flatter, with a peak reduction, increasing spread, and a mean value shift to the right relative to the historical curve. Its median does not show significant changes in magnitude and frequency for the near-future, mid-future, and far-future periods under SSP2-4.5 scenario. On the contrary, projection of the SWMR shows distinct increases (decreases) in magnitude (frequency). Therefore, we expect more (less) extreme rainfall in the wet season (dry season) from the near-future to the far-future periods.

CONCLUSIONS AND DISCUSSION
The present study analyzes the changes in mean tempera- (5) Different projected increase in the seasonal Monsoon rainfall are found over each country in CLMVT (Table 3). Overall, the largest (smallest) increases in SWMR and NEMR are found over Vietnam (Thailand) and Cambodia (Thailand), respectively, for the farfuture period under SSP5-8.5 scenario. The projection of the NEMR does not fit well the Gamma pdf as compared to the SWMR ( Figure 11). The pdf curve during the southwest Monsoon season becomes flatter, with a peak reduction, increasing spread, and a mean value shift to the right relative to the historical curve. Projection of the SWMR shows distinct increases (decreases) in magnitude (frequency). Therefore, we expect more (less) extreme rainfall in the wet season (dry season) from the near-future to the far-future periods.
Due to the temporarily limited number of available CMIP6 models which will be gradually released by the Scen-