Evaluating CMIP6 model performance of wet and dry spells by using novel climate rainfall indices over the Southeast Asia Region

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This study evaluates the performance of CMIP6 models in simulating precipitation indices, focusing on both spatial patterns and temporal variations from 1981 to 2010. Models were classified into two groups: the 'Good_group,' which demonstrated robust performance, and the 'Bad_group,' which exhibited notable biases. The Good_group comprising ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, MIROC6, NorESM2-LM, and NorESM2-MM consistently performed better in simulating total precipitation (PRCPTOT), consecutive dry days (CDD), and daily intensity (SDII). These models showed smaller biases and more accurate representations of spatial patterns and temporal variability in the SEA region. Conversely, the Bad_group including EC-Earth3, EC-Earth3-Veg, GFDL-ESM4, and MPI-ESM1-2-LR exhibited significant biases and poorer performance. Specifically, Good_group models provided a more realistic simulation of PRCPTOT and SDII with reduced biases, whereas Bad_group models showed larger errors, especially in northeastern India and Myanmar. For CDD, Good_group models estimated fewer consecutive dry days than observed, while Bad_group models overestimated them. Despite advancements in CMIP6 models, including higher resolutions and improved parameterizations, challenges persist in accurately simulating wet spells dynamics in the complex SEA region. This study identifies the most skilful models and areas for improvement, offering valuable insights for model selection and enhancing climate projections and adaptation strategies in the SEA region. Climate modelling CMIP6 models Southeast Asia Extreme precipitation Model evaluation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction It is well-documented that the world's climate has changed significantly, with a noticeable warming trend over the last century (Hollingsworth and Ingmann, 1999; IPCC, 2018 ). This warming has accelerated the hydrological cycle and adjusted general circulation patterns, resulting in a marked increase in extreme precipitation events. The Southeast Asia (SEA) region, characterized by its complex climate dynamics, is particularly affected by these changes. This region is influenced by multiple monsoon systems, including the Indian summer monsoon and the western North Pacific summer monsoon (Chen and Yoon, 2000 ; Feng et al., 2020 ; Oo, 2023). Additionally, the unique topography and intricate air–sea interactions further complicate the spatiotemporal variations of precipitation and extreme precipitation in the SEA region (Buckley et al., 2014 ; Ge et al., 2017 sänen et al., 2016). Given that extreme precipitation events significantly impact economic activities and human lives in the SEA region, understanding and quantifying these events is crucial for implementing effective measures to mitigate associated risks (Kim and Kug, 2018 ; Ramarao et al., 2015 ; Zhai and Zhuang, 2009). Global climate models are essential tools for simulating current climate dynamics and projecting future climate scenarios (Orlowsky and Seneviratne, 2012 ; Samuels et al., 2017 ; Wang et al., 2017 ; Rao et al., 2019 ). Despite advancements, these models often struggle to accurately capture the features of extreme precipitation due to the complexity of its temporal and spatial variability (Kim et al., 2018). A comprehensive analysis by Sillmann ( 2013 ) revealed that Coupled Model Intercomparison Project Phase 5 (CMIP5) models generally overestimate extreme precipitation while underestimating consecutive wet days. This issue persists from CMIP3 through CMIP5, with significant uncertainties in simulating extreme precipitation in the tropics and subtropics (Sillmann, 2013 ; Toreti et al., 2013 ). Studies comparing models with satellite data have shown that many CMIP3 models underestimate the intensity of extreme precipitation in response to temperature, particularly in tropical regions (Allan et al., 2010; Donat et al., 2016 ). Similarly, Raghavan et al. ( 2018 ) found that a subset of CMIP5 models struggled to reproduce the historical climate of Southeast Asia, and Li and Zhao ( 2019 ) demonstrated that most CMIP5 models overestimate annual mean precipitation in the region. In China, although CMIP5 models capture the basic spatial and temporal characteristics of extreme precipitation in eastern China, they exhibit a wet bias in North China and a dry bias in South China (Jiang et al., 2015 ; Ou et al., 2013 ; Sun et al., 2016 ). Recent advancements in global climate modelling, particularly with the release of Phase 6 of the Coupled Model Intercomparison Project (CMIP6) models, offer enhanced resolution and improved parameterization for cloud microphysical processes and biogeochemical interactions (Eyring et al., 2016 , 2019). These advancements are expected to result in better simulation accuracy for historical climate conditions (Mishra et al., 2021 ). However, while substantial research has focused on model evaluation and bias analysis in regions such as North America and Europe, there is a notable lack of similar studies for the SEA region (Misra and DiNapoli, 2013 ; Nguyen-Le et al., 2015 ; Tangang et al., 2020 ). Quantitative analyses of extreme precipitation in the SEA region remain sparse (Raghavan et al., 2018 b). It is crucial to assess how effectively each CMIP6 model simulates the spatial patterns and temporal variability of extreme precipitation in this region and to identify the most skillful models based on performance metrics. Establishing quantitative indicators will enable an evaluation of each model's ability to replicate observed precipitation features and comparing skillful and less skillful model ensembles will provide insights for improving extreme precipitation simulations in the SEA region. The precipitation and extreme precipitation mechanisms in the SEA region are complex and varied (Buckley et al., 2014 ; Hirabayashi et al., 2013 ; Wang and Magnusdottir, 2005). Given these complexities, evaluating the performance of climate models in simulating extreme precipitation indices should be done separately for different subregions within the SEA. This approach offers a more accurate reference for model selection. Annual extreme precipitation indices are commonly used and widely accepted for such evaluations (Alexander and Arblaster, 2017 ; Ge et al., 2021; Zhu et al., 2020b). This study aims to quantitatively assess how well CMIP6 models simulate the spatial patterns and temporal variations of present-day precipitation and extreme precipitation in the SEA region using various skill score metrics on an annual basis. The study will classify models into different ensembles based on their performance across subregions, identifying the most and least skillful models. The final goal is to analyze the potential causes of biases by comparing these model ensembles with observational data. 2 Materials and methods 2.1 Study Area Description The SEA region, located between 10°–30°N and 80°–120°E, encompasses parts of South Asia and Southeast Asia, including the coastal regions of Myanmar, Thailand, Bangladesh, and India. This region lies at the intersection of the Bay of Bengal and the South China Sea, making it a crucial area for understanding regional climate dynamics. The SEA's climate is strongly influenced by the Asian monsoon system, which governs seasonal variations in precipitation. Additionally, the region plays a significant role in modulating the retreat and advance of the Western Pacific Subtropical High in the Northern Hemisphere (Li et al., 2019). Given its tropical monsoon climate, the SEA region is prone to extreme precipitation events, which have been observed to intensify in recent years (Oo et al., 2023). The increasing trend in regional rainfall intensity highlights the importance of accurately simulating precipitation patterns for climate research. Consequently, evaluating how well CMIP6 models capture these climatic characteristics is critical for understanding the spatial and temporal variability of rainfall and extremes in the region. This evaluation will not only inform climate prediction efforts but also aid in the development of effective climate adaptation and mitigation strategies, especially in a region vulnerable to both monsoonal fluctuations and the broader impacts of climate change. 2.2 Data source and acquisition Daily precipitation data from 19 CMIP6 models for the period 1981–2010 were utilized to calculate three extreme precipitation indices PRCPTOT (total precipitation), SDII (simple daily intensity index), and CDD (consecutive dry days) over the SEA region, in order to identify the most accurate models for future regional climate prediction (You et al., 2018). Basic information for each model, including the model name, modelling center and atmospheric resolution, is provided in Supplementary S-1, with further details available online ( https://www.wcrp-climate.org/etccdi ). In model evaluation, gridded datasets that incorporate homogenization and interpolation techniques are considered more reliable than station observations (Alexander et al., 2006). Accordingly, the daily gridded precipitation dataset from the Asian Precipitation–Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) project was employed as the observational benchmark. This dataset, with a spatial resolution of 0.25° × 0.25° for the period 1981–2010, covers the region 15°S–55°N and 60°E–150°E (available at http://aphrodite.st.hirosaki-u.ac.jp/download/ ) (Yatagai et al., 2012). It was found that the APHRODITE data underestimated the precipitation amount and overestimated the number of rain days for the entire study region (Ji et al., 2020 ). To ensure consistency, the extreme precipitation indices calculated from both observational data and model outputs were first computed on their respective native grids and subsequently interpolated to a common 1° × 1° latitude-longitude grid using bilinear interpolation, following the methods of Kharin et al. (2013), Oo et al. (2023), and Ou et al. ( 2013 ). 2.3 Extreme Precipitation Indices Following the definitions established by the Expert Team on Climate Change Detection and Indices (ETCCDI; Zhang et al., 2011), this study employed several extreme precipitation indices (Supplementary S-2) to evaluate the SEA region. The selected indices include total wet-day precipitation (PRCPTOT), the simple daily intensity index (SDII), and consecutive dry days (CDD) (Fig. 2 ). These indices capture fundamental aspects of precipitation and extremes and have been widely applied in previous studies (Alexander and Arblaster, 2009; Jiang et al., 2015 ; Mie et al., 2022; Oo et al., 2023; Sillmann et al., 2013). Additional details on these extreme precipitation indices can be accessed via the ETCCDI website ( http://etccdi.pacificclimate.org/indices.shtml ). 2.3.1 Total Wet-Day Precipitation (PRCPTOT) Total wet-day precipitation (PRCPTOT) refers to the cumulative precipitation on days when the recorded precipitation is equal to or greater than 1 mm. This index offers a comprehensive measure of the total precipitation that occurs during wet days over a defined period. $$\:PRCPTO{T}_{j}={\sum\:}_{n=1}^{N}{RR}_{ij}$$ Where \(\:{RR}_{ij}\) is the daily precipitation amount on day i in period j, and N represents the number of wet days in period j. 2.3.2 Simple Daily Intensity Index (SDII) The Simple Daily Intensity Index (SDII) quantifies the average intensity of precipitation on wet days. It is determined by dividing the total precipitation amount by the number of wet days over a given period. $$\:{SDII}_{j}=\frac{{\sum\:}_{w=1}^{w}{RR}_{wj}}{W}$$ Where \(\:{RR}_{wj}\) is the daily precipitation amount on a wet day \(\:w(RR\ge\:1.0mm)\) in period j. W represents the number of wet days in j. 2.3.3 Consecutive Dry Days (CDD) Consecutive Dry Days (CDD) represent the maximum number of consecutive days with daily precipitation less than 1 mm. This index serves as an indicator of drought conditions. $$\:CDD=\text{m}\text{a}\text{x}(consercutive\:days\:with\:{RR}_{ij}<1mm)$$ It is important to note that the calculation of these three extreme precipitation indices PRCPTOT, SDII, and CDD for a given period (month, year, or rainy season) was based on daily data from each year within the 1981–2010 timeframe. 2.4 Model Performance Metrics 2.4.1 Taylor Diagram We utilized the Taylor diagram to assess the models' performance in reproducing the observed spatial patterns of extreme precipitation indices in the SEA region. The Taylor diagram facilitates a succinct comparison between model simulations and observations by incorporating the pattern correlation coefficient, centered pattern root-mean-square (RMS) difference, and the ratio of the spatial standard deviations of modelled to observed values (Taylor, 2001). A model is considered more skillful in replicating the spatial patterns of observed extreme precipitation indices if the centered RMS difference is closer to 0, and both the spatial correlation and the ratio of spatial standard deviations approach 1. This approach provides both a visual and quantitative evaluation of the models' ability to capture the spatial characteristics of extreme precipitation events in the SEA region. 2.4.2 Interannual variability skill score (IVS) $$\:IVS=(\frac{{STM}_{m}}{{STD}_{o}}-\frac{{STD}_{o}}{{STD}_{m}}{)}^{2}$$ where \(\:{STD}_{m}\) and \(\:{STD}_{o}\) are the interannual standard deviations of the model simulations and observations, respectively. The calculations of STDm and STDo are based on yearly data (Jiang et al., 2015 ;Ren et al., 2017 ). A smaller IVS value implies better model simulation. 2.4.3 Comprehensive Rating Metrics Based on the Taylor diagram and Interannual Variability Skill Score (IVS) values, each extreme precipitation index was employed to rank the models according to their spatial and temporal simulation capabilities. A comprehensive ranking metric (MR; Jiang et al., 2015 ) was computed separately for spatial and temporal simulation capabilities. The MR is defined as follows: $$\:MR=1-\frac{1}{nm}\sum\:_{i=1}^{n}{rank}_{i}$$ where m is the number of models, n is the number of extreme precipitation indices, and \(\:{rank}_{i}\) is the model rank for the ith extreme precipitation index. Here, the maximum value of \(\:{rank}_{i}\) is 40, and the minimum value is 1. The better the model performance, the smaller the value of \(\:{rank}_{i}\) . Therefore, the closer MR is to 1, the better the model performance is. 2.5 Regional Averages of the Four Extreme Precipitation Indices Over the Subregions To evaluate the models' ability to simulate extreme precipitation indices in the SEA region, relative errors between the observed data and model outputs were calculated for each subregion. $$\:Relative\:error=\frac{Modeled-observed}{observed}\times\:100$$ Figure 3 displays a color-coded "portrait diagram" illustrating the relative errors for each extreme precipitation index across all models. In this diagram, both the color shading and numerical values within each box represent the magnitude of the relative error. 2.5.1 Model evaluation The performance of each model in reproducing both the spatial patterns and temporal variability of the extreme precipitation indices in the SEA region was evaluated independently. 3 Results and discussion 3.1 Important features observed 3.1.1 Overestimation in Precipitation Metrics Most CMIP6 models tend to overestimate annual precipitation, annual extreme precipitation, and precipitation intensity in the SEA region, as indicated by positive relative errors for PRCPTOT, CDD, and SDII in the majority of models. These findings are consistent with previous research (Zhu et al., 2020a). Figure 3 displays the relative errors for three extreme precipitation indices—PRCPTOT, SDII, and CDD—simulated by 19 CMIP6 models, averaged over a specified region for the period 1981–2010. The Mean Absolute Error (MAE) for each model is also shown in the final column. This analysis reveals the strengths and weaknesses of the CMIP6 models in simulating extreme precipitation events, which is critical for understanding and predicting climate impacts in the region. 3.1.2 Variability in Relative Errors The relative errors for PRCPTOT varied significantly among the models. INM-CM4-8 exhibited the largest positive relative error at 18%, while KIOST-ESM had the most pronounced negative relative error at -24%. Most models tended to underestimate PRCPTOT, as indicated by the prevalence of negative errors. For SDII, the models consistently underestimated precipitation intensity, with errors ranging from − 10% (KACE-1-0-G) to -41% (KIOST-ESM). KIOST-ESM showed the most substantial underestimation, reflecting a notable discrepancy in the simulation of daily precipitation intensity. Regarding CDD, the models showed a wide range of errors, from − 53% (INM-CM4-8) to 92% (MPI-ESM1-2-HR). INM-CM4-8 and INM-CM5-0 significantly underestimated CDD, while MPI-ESM1-2-HR and MPI-ESM1-2-LR overestimated it by a large margin. The Mean Absolute Error (MAE), summarizing the overall error for each model, ranged from 8.7 (ACCESS-CM2) to 46 (KIOST-ESM). Models with lower MAE values, such as ACCESS-CM2 (8.7) and NESM3 (9.8), demonstrated better performance in simulating extreme precipitation indices. In contrast, KIOST-ESM (46) and INM-CM4-8 (36) exhibited higher MAE, indicating greater overall errors in simulating the observed data. The ACCESS-CM2, NESM3, and BCC-CSM2-MR models exhibited relatively lower errors across the indices, indicating they are more reliable for simulating extreme precipitation events in the SEA region. In contrast, KIOST-ESM and INM-CM4-8 showed higher errors, suggesting these models require improvement for more accurate extreme precipitation simulations. The consistent underestimation of SDII by most models highlights a general difficulty in capturing daily precipitation intensity accurately. For CDD, the models displayed substantial variability, with some significantly underestimating or overestimating dry spells, indicating challenges in accurately simulating prolonged dry periods. Overall, most CMIP6 models performed relatively well in reproducing the extreme precipitation indices, with smaller relative errors. To better assess model performance, the mean absolute error (MAE) of the relative errors (shown in the right-hand column of Fig. 3 ) was calculated for each model. Based on MAEs, models such as ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, MIROC6, and NESM3 demonstrated the highest skill in the study area, with relative errors below 15%. However, most models still performed moderately, with errors below 30%, except for INM-CM4-8, MPI-ESM1-2-LR, MPI-ESM1-2-HR, and KIOST-ESM, which exhibited higher relative errors. 3.1.3 Taylor Diagram The Taylor diagrams for the three extreme precipitation indices (PRCPTOT, CDD, and SDII) using 19 CMIP6 models provide a comprehensive assessment of model performance by comparing them with observations. These diagrams focus on three key metrics: the correlation coefficient, standard deviation, and root mean square deviation (RMSD). They offer insights into the strengths and weaknesses of the models in simulating various aspects of extreme precipitation events. Most models showed moderate to high correlation with observations for PRCPTOT, with correlation coefficients generally ranging from 0.4 to 0.8. This indicates that the models capture the spatial patterns of total precipitation relatively well. The standard deviation for most models was close to 1, suggesting that they simulated the variability of total precipitation accurately. Models such as ACCESS-CM2 and BCC-CSM2-MR demonstrated better performance, with relatively high correlation and standard deviations close to 1, indicating a strong match with observations. For CDD, the models generally exhibited lower correlation coefficients compared to PRCPTOT, with values mostly between 0.1 and 0.5. This suggests that the models struggle to capture the spatial patterns of dry days accurately. The standard deviation for many models deviated from 1, indicating discrepancies in the simulation of dry day variability, with some models either overestimating or underestimating this variability. For instance, while MPI-ESM1-2-LR and GFDL-ESM4 exhibited higher correlation, they also showed significant deviations in standard deviation, suggesting a trade-off between spatial pattern accuracy and amplitude consistency. For SDII, the models displayed moderate to high correlation coefficients, generally ranging from 0.4 to 0.7, indicating that they reasonably capture daily precipitation intensity. The standard deviation for SDII was close to 1 in many models, suggesting that they accurately simulated the variability in daily precipitation intensity. CanESM5 and MRI-ESM2-0 showed particularly strong performance, with relatively high correlations and standard deviations close to 1, indicating more accurate simulations of daily precipitation intensity. The PRCPTOT index was generally better captured by the models in terms of both correlation and standard deviation, indicating stronger overall performance in simulating total precipitation. The CDD index presented the greatest challenge, with models showing lower correlations and higher variability in standard deviation, reflecting difficulties in accurately simulating dry spells. The SDII index was moderately well-simulated, with several models exhibiting high correlation and accurate variability, though some still showed deviations. ACCESS-CM2 and BCC-CSM2-MR consistently performed well across multiple indices, highlighting their robustness in simulating extreme precipitation events. Models like MPI-ESM1-2-HR and GFDL-ESM4 performed well in certain indices but exhibited trade-offs in others, such as overestimating or underestimating variability while maintaining reasonable correlation. KIOST-ESM and INM-CM4-8, which showed higher errors in previous analyses, also demonstrated poorer performance in the Taylor diagrams, particularly in terms of correlation for indices like CDD. 3.2 Evaluation of Spatial Variation The climatological spatial distributions of the three extreme precipitation indices, based on observations and 19 CMIP6 models for the period 1981–2010, are presented. The observed spatial patterns are generally consistent across the indices, with large areas of high annual precipitation, extreme precipitation, and precipitation intensity concentrated along the coastal regions and key areas of the SEA region. However, the highest numbers of consecutive dry days (CDD) are confined to specific areas. Most models struggle to capture the observed spatial patterns of all four extreme precipitation indices. To provide a concise summary of the models' performance in simulating the spatial patterns of these indices, Taylor diagrams (Fig. 4 ) were used. The spatial correlation coefficients for most CMIP6 models range between 0.3 and 0.9 for SDII in the SEA region. However, for R95p and CDD, a few models have spatial correlation coefficients below 0.3 (Figs. 4 b, c), and nearly all models perform poorly in simulating PRCPTOT (Fig. 4 a). This indicates that CMIP6 models have limited skill in simulating extreme precipitation indices, except for SDII in the SEA region. In certain subregions within the SEA, the spatial correlation coefficients for CDD are greater than 0.3 for all CMIP6 models (Fig. 4 b), while three-quarters of the models fail to simulate the spatial patterns of PRCPTOT and SDII, with spatial correlation coefficients below 0.3 (Figs. 4 a, b, c). These findings suggest that CMIP6 models only simulate the spatial distribution of CDD effectively in certain subregions. Most models show a ratio of variance less than 1 for PRCPTOT and SDII (Figs. 4 a–c), while fewer models show a ratio of variance approaching 1 for CDD, indicating that most models underestimate the spatial variability of annual precipitation, extreme precipitation, and precipitation intensity, but overestimate the spatial variability of consecutive dry days. For other subregions, most models exhibit a variance ratio of less than 1 for PRCPTOT and SDII, while the variance ratio is generally greater than 1 for CDD. Only a few models show a variance ratio for PRCPTOT approaching 1. This suggests that most models underestimate the spatial variation of extreme precipitation and precipitation intensity but overestimate the variability of consecutive dry days in these subregions. Across both the SEA region and its subregions, the majority of models show centered normalized RMS differences (indicated by grey solid lines) for PRCPTOT that are larger than 1 (Fig. 4 a), indicating substantial bias in simulating annual extreme precipitation. The relatively concentrated distributions in the Taylor diagrams suggest a relatively small inter-model spread for all indices over the SEA region (Figs. 4 a–c). Overall, the CMIP6 models perform relatively poorly in simulating the spatial patterns of extreme precipitation indices in the SEA region. Among the indices, SDII is better simulated in some subregions, while CDD is well captured in other areas of the SEA region. In general, the spatial variations of extreme precipitation indices are more accurately simulated in certain subregions than in others. 3.3 Evaluation of Interannual Variability In addition to spatial patterns, evaluating the models' ability to simulate the temporal variability of extreme precipitation indices is crucial for comprehensive model assessment. To this end, the Interannual Variability Skill (IVS) score was employed to quantify the similarity between the interannual variability of the modelled and observed extreme precipitation indices. IVS scores for the three extreme precipitation indices, averaged over the subregions of the SEA region, were calculated for the period 1981–2010 (Fig. 5 ). The interannual variability of the indices was better simulated in certain subregions, as indicated by the narrower range of IVS values. For example, the IVS scores for PRCPTOT, CDD, and SDII ranged from 0 to 1 in some subregions, while CDD exhibited more variability, ranging from 0 to 1.7 in other parts of the SEA region. In specific subregions, the interannual variability of SDII was well captured by most CMIP6 models, with all models showing good performance in simulating the observed interannual variability of SDII. Models such as BCC-CSM2-MR and ACCESS-ESM1-5 stood out with relatively higher IVS values, indicating superior performance in simulating the interannual variability of total precipitation. In contrast, many other models exhibited lower IVS scores, suggesting difficulties in accurately capturing this variability. Notably, GFDL-ESM4, MRI-ESM2-0, and MPI-ESM1-2-LR achieved higher IVS scores for CDD, with scores exceeding 1.0, indicating strong performance in simulating the variability of dry days. However, a significant number of models showed lower or negligible IVS scores for CDD, indicating poor performance in capturing the interannual variability of consecutive dry days. For SDII, models such as ACCESS-CM2, BCC-CSM2-MR, and MIROC6 displayed moderate IVS scores, reflecting decent performance in simulating the interannual variability of daily precipitation intensity. Similar to PRCPTOT, most models struggled to simulate the year-to-year variability of SDII, as evidenced by lower IVS scores. GFDL-ESM4 and MRI-ESM2-0 particularly excelled in simulating the interannual variability of CDD, demonstrating strength in this area. Models like ACCESS-CM2 and BCC-CSM2-MR showed consistent performance across multiple indices, highlighting their robustness in capturing the interannual variability of extreme precipitation events in the region. However, many models exhibited lower IVS scores for both PRCPTOT and SDII, underscoring the challenge these models face in accurately simulating interannual variability. The IVS analysis reveals significant variations in model performance when simulating the interannual variability of extreme precipitation indices. While models such as GFDL-ESM4 and MRI-ESM2-0 excel in specific indices (e.g., CDD), others like ACCESS-CM2 and BCC-CSM2-MR perform consistently well across all indices. Nonetheless, many models show limitations, particularly in simulating the variability of total precipitation and daily precipitation intensity, highlighting areas for further improvement in climate modelling. 3.4 Overall Model Ranking Due to inconsistencies between model rankings based on spatial patterns and interannual variability of extreme precipitation indices, it is crucial to consider both aspects in the evaluation process. This study highlights that the alignment between spatial and temporal simulations varies across different subregions of the SEA. To address this, models were categorized into two ensembles based on their performance in both spatial and temporal dimensions. The criteria were: (i) Models with mean rankings (MRs) above 0.6 for both spatial and temporal dimensions were classified as part of the "Good_group," while (ii) Models with MRs below 0.6 for both dimensions were classified as part of the "Bad_group." According to these criteria, six models - ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, MIROC6, NorESM2-LM, and NorESM2-MM - were identified as the most skillful, demonstrating strong performance in both spatial and temporal simulations. Conversely, four models—EC-Earth3, EC-Earth3-Veg, GFDL-ESM4, and MPI-ESM1-2-LR - were classified as less skillful, showing deficiencies in both aspects. Generally, the model evaluation for the SEA region identified a group of six high-performing models and four less effective models. This classification provides a clearer perspective on model performance and aids in selecting the most suitable models for further research and applications related to extreme precipitation in the SEA region. 3.5 Performance of Optimal Models In the previous section, we identified the most and least skillful models for simulating extreme precipitation indices in the SEA region. This section compares the performance of the Good_group and Bad_group across each extreme precipitation index for the period 1981–2010, based on relative errors presented in box-and-whisker plots (Fig. 7 ). For the first subregion, simulations of PRCPTOT and SDII by the better-performing group (denoted as Good) demonstrated notable improvements compared to the lower-performing_group (denoted as bad), as indicated by median values closer to zero, smaller ranges, and reduced interquartile ranges. The Good_group also showed some improvement in simulating CDD, though to a lesser extent. Figure 8 further illustrates the spatial patterns and biases of the two model ensembles (Good_group and Bad_group) for the four extreme precipitation indices across the SEA region. The model biases represent the differences between the two model ensembles and observations for the respective subregions. The observed climatological spatial distributions are consistent for PRCPTOT (Fig. 8 a), CDD (Fig. 8 b), and SDII (Fig. 8 c), with high values of annual precipitation and extreme precipitation concentrated along coastal regions and other key areas of the SEA region, while lower values are found in specific areas. High CDD values are primarily located in certain regions. For the first subregion, the better-performing Good group exhibited smaller biases compared to the lower-performing Bad group for PRCPTOT, CDD, and SDII, particularly in specific regions. However, the Good_group showed less annual total precipitation and extreme precipitation in certain areas of the first subregion compared to the Bad_group, which exhibited a wet bias. Additionally, the Good_group significantly reduced the wet bias over most of the second subregion compared to the Bad_group. For example, the mean absolute errors for CDD in the Bad_group reached up to 80 days, while they were less than 80 days in the Good_group for the second subregion. Overall, the Good_group models demonstrated significantly better performance in simulating extreme precipitation indices, exhibiting smaller biases and more accurate spatial and temporal representations in the SEA region. The historical interannual cycles for PRCPTOT, CDD, and SDII, averaged over the SEA region, are shown in Fig. 9 . The red lines represent the ensemble means of all CMIP6 models, Good_group, and Bad_group, while the black dotted lines indicate the observed interannual cycles. All ensemble means capture the basic characteristics of the interannual cycles for PRCPTOT, CDD, and SDII. However, for the first subregion, the PRCPTOT (Fig. 9 a), CDD (Fig. 9 b), and SDII (Fig. 9 c) simulated by the Good_group ensemble mean is more realistic compared to those of the other two ensemble means. The figure depicts the spatial distribution of relative errors and biases for two model ensembles, "Good" and "Bad," across three extreme precipitation indices - PrcpTOT (total precipitation), CDD (consecutive dry days), and SDII (simple daily intensity index) - over the SEA region. For the PrcpTOT index, the "Good" ensemble (Fig. 10 .a) showed a mix of positive and negative biases, with significant positive biases in northeastern India and Myanmar, and negative biases in parts of northern India. In contrast, the "Bad" ensemble (Fig. 10 .b) displayed an overall increase in positive biases, particularly in the northern Bay of Bengal and extending into Myanmar and Thailand. Regarding the CDD index, the "Good" ensemble (Fig. 10 .c) revealed negative biases across most regions, indicating fewer consecutive dry days than observed. Conversely, the "Bad" ensemble (Fig. 10 .d) exhibited widespread positive biases, reflecting an overestimation of consecutive dry days, especially in central Myanmar and northern Southeast Asia. For the SDII index, the "Good" ensemble (Fig. 10 .e) showed moderate positive biases across the region, with notable increases along coastal Myanmar. The "Bad" ensemble (Fig. 10 .f) exacerbated these biases, particularly in the northeastern areas, indicating an overestimation of daily precipitation intensity. The dotted areas in the center and right columns represent regions where the differences are statistically significant at the 95% confidence level, highlighting the more pronounced errors in the "Bad" ensemble compared to the "Good" ensemble. In analysing water vapor transport and convergence, the "Good" group models generally showed smaller biases in water vapor convergence compared to the "Bad" group. They also better simulated the spatial variation and interannual variability of specific humidity and vertical velocity during the rainy season, evidenced by larger pattern correlation coefficients and smaller centered normalized RMS differences and IVS skill scores. This leads to more accurate simulations of precipitation and extreme precipitation. While higher resolution alone doesn’t guarantee better skill, the “Good” models outperform the “Bad” ones due to smaller biases in water vapor convergence and better simulations of key processes like spatial variation, interannual variability of specific humidity, and vertical velocity during the rainy season. These strengths are evident from higher pattern correlation coefficients, smaller normalized RMS differences, and improved IVS skill scores. Interestingly, while higher resolution can enhance the simulation of fine-scale processes (as noted by Diffenbaugh et al., 2008 , it’s not the main determinant of model skill in the SEA region. Many “Good” models achieve better accuracy despite not having high resolution, indicating that other factors, such as parameterization schemes and model dynamics, play a crucial role. 4 Conclusion and recommendations This study provides a comprehensive evaluation of CMIP6 models in simulating extreme precipitation indices over the SEA region. The analysis differentiated between two model ensembles, "Good" and "Bad," based on their ability to accurately represent both spatial patterns and temporal variability of extreme precipitation events. The findings reveal that models within the "Good" ensemble generally exhibited superior performance compared to those in the "Bad" ensemble. Specifically, the "Good" models showed smaller biases and more accurate spatial representations for indices such as total precipitation (PrcpTOT), consecutive dry days (CDD), and daily precipitation intensity (SDII). Additionally, these models demonstrated a better alignment with observed interannual variability, particularly for CDD and SDII. Conversely, the "Bad" models displayed substantial discrepancies in both spatial patterns and temporal variability, with higher relative errors and less precise simulations. The study also highlighted that, despite advancements in model resolution, horizontal resolution did not appear to be a primary factor in determining model performance in simulating extreme precipitation. This suggests that other elements, such as model physics and parameterization, play a more critical role in achieving accurate simulations. To enhance the accuracy of extreme precipitation simulations, several steps should be considered. Firstly, there is a need to refine models to better capture extreme precipitation events, with particular emphasis on improving the simulation of consecutive dry days (CDD) and daily precipitation intensity (SDII). Enhancing the representation of water vapor transport and moisture conditions is also crucial, as accurate simulation of these factors contributes to more realistic precipitation predictions. When selecting models for future studies or practical applications related to extreme precipitation in the SEA region, it is advisable to prioritize those models that have demonstrated consistent strength across both spatial and temporal dimensions. The "Good" models identified in this study should be favoured for their overall robustness and accuracy. Further research should focus on understanding how different model physics and parameterizations affect the simulation of extreme precipitation. This understanding can guide refinements to models, enabling them to better capture fine-scale climate processes. Additionally, while higher resolution models did not universally outperform lower resolution models in this study, ongoing evaluation of the impact of resolution on simulation accuracy remains important. Continued investigation into the role of resolution can help optimize model configurations for various contexts. By following these recommendations, future climate modelling efforts can improve the precision of extreme precipitation predictions and enhance the understanding of climate variability in the SEA region. Declarations Conflict of Interest: The authors have no competing interests to declare. Data Statement: The datasets generated and utilized in this study are available from the corresponding author upon request. Funding: This research is supported by National Natural Science Foundation of China (No. 32361143869). Author Contributions: Thet Mar Soe: Conceptualization, Data curation, Methodology, Writing-Original draft preparation, Visualization. Abraham Okrah: Conceptualization, Data curation, Methodology, Writing-Original draft preparation, Kyaw Than Oo: Conceptualization, Data curation, Methodology, Visualization Reviewing and Editing. Ebaju Gerverse Kamukama: Data curation, Methodology, Writing-Original draft preparation, Reviewing and Editing. Fangmin Zhang: Conceptualization, Data Curation, Methodology, Supervision, Reviewing and editing. Acknowledgement: The first author [Thet Mar Soe], expresses sincere gratitude to the World Meteorological Organization-Chinese Scholarship Council and Nanjing University of Information Science and Technology, Nanjing-China, for their support in sponsoring her Master's studies. References Alexander LV, Arblaster JM (2017) Assessing changes in extreme precipitation: A global and regional perspective. J Clim 30(24):9472–9488. https://doi.org/10.1175/JCLI-D-17-0357.1 Allan RP, Soden BJ (2010) Atmospheric warming and the amplification of precipitation extremes. 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Clim Dyn 47(5–6):1653–1667. https://doi.org/10.1007/s00382-015-2904-5 Tangang FK, Shamsudin MN, Ma R (2020) The performance of CMIP6 models in simulating the monsoon climate over Southeast Asia. Clim Dyn 54(5):2307–2325. https://doi.org/10.1007/s00382-019-05060-6 Toreti A, Deser C, Sinsky E (2013) Model biases in simulating precipitation extremes in the tropics and subtropics. J Clim 26(22):8736–8750. https://doi.org/10.1175/JCLI-D-12-00598.1 Wang S, Zhang X, Zhou L (2017) Evaluation of extreme precipitation events using CMIP Diffenbaugh NS, Giorgi F, Pal JS (2008) Climate change hotspots in the United States. Geophys Res Lett 35(16):1–5. https://doi.org/10.1029/2008GL035075 Ji X, Li Y, Luo X, He D, Guo R, Wang J, Bai Y, Yue C, Liu C (2020) Evaluation of bias correction methods for APHRODITE data to improve hydrologic simulation in a large Himalayan basin. Atmos Res 242(February):104964. https://doi.org/10.1016/j.atmosres.2020.104964 Jiang Z, Li W, Xu J, Li L (2015) Extreme precipitation indices over China in CMIP5 models. Part I: Model evaluation. J Clim 28(21):8603–8619. https://doi.org/10.1175/JCLI-D-15-0099.1 Mishra V, Aadhar S, Mahto SS (2021) Anthropogenic warming and intraseasonal summer monsoon variability amplify the risk of future flash droughts in India. Npj Clim Atmospheric Sci 4(1). https://doi.org/10.1038/s41612-020-00158-3 Ren YY, Ren GY, Sun XB, Shrestha AB, You QL, Zhan YJ, Rajbhandari R, Zhang PF, Wen KM (2017) Observed changes in surface air temperature and precipitation in the Hindu Kush Himalayan region over the last 100-plus years. Adv Clim Change Res 8(3):148–156. https://doi.org/10.1016/J.ACCRE.2017.08.001 Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Published Journal Publication published 10 Nov, 2025 Read the published version in Climate Dynamics → Version 1 posted Reviewers agreed at journal 06 Dec, 2024 Reviewers invited by journal 05 Dec, 2024 Editor assigned by journal 05 Dec, 2024 First submitted to journal 04 Dec, 2024 Editorial decision: Major Revision 02 Dec, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5171746","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":386888504,"identity":"0027a223-c03a-4a5c-a746-213454e3d88e","order_by":0,"name":"Thet Mar Soe","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Thet","middleName":"Mar","lastName":"Soe","suffix":""},{"id":386888505,"identity":"7e69c710-bbe2-4a7d-a7e4-d757fc5e9c90","order_by":1,"name":"Abraham Okrah","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0006-8049-7310","institution":"NUIST: Nanjing University of Information Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Abraham","middleName":"","lastName":"Okrah","suffix":""},{"id":386888506,"identity":"432734ac-1b03-4906-8f1c-f7f29b1aa670","order_by":2,"name":"Kyaw Than Oo","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology School of Applied Meteorology","correspondingAuthor":false,"prefix":"","firstName":"Kyaw","middleName":"Than","lastName":"Oo","suffix":""},{"id":386888507,"identity":"79a2198b-68c5-4bdc-bc52-d92f92f26676","order_by":3,"name":"Ebaju Gerverse Kamukama","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ebaju","middleName":"Gerverse","lastName":"Kamukama","suffix":""},{"id":386888508,"identity":"7dd16913-a05c-4f8f-ba8c-e90835920c40","order_by":4,"name":"Fangmin Zhang","email":"","orcid":"","institution":"Nanjing University of Information Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Fangmin","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-09-28 18:32:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5171746/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5171746/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00382-025-07938-8","type":"published","date":"2025-11-10T15:57:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70937421,"identity":"fff916bb-1525-4d96-990e-5976e6083f7a","added_by":"auto","created_at":"2024-12-09 11:15:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":223322,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Map illustrating the annual total rainfall (mm) across the study region, displayed alongside longitude and altitude contours; and (b) the corresponding time series of annual rainfall, including the calculated trend value.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5171746/v1/0a6e2ad656cdf3857c67055e.png"},{"id":70937643,"identity":"783c3221-5d9c-4240-92f5-4d7207facc2f","added_by":"auto","created_at":"2024-12-09 11:23:08","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":228840,"visible":true,"origin":"","legend":"\u003cp\u003eInterannual time series color strip of (a) PRCPTOT, (b) CDD, and (c) SDII for observations and CMIP6 models from 1981 to 2010.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5171746/v1/2711cef747202845b078ba6d.png"},{"id":70937640,"identity":"d2cece84-ff71-4e65-9722-948113882ee9","added_by":"auto","created_at":"2024-12-09 11:23:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":169854,"visible":true,"origin":"","legend":"\u003cp\u003eRelative errors of three extreme precipitation indices simulated by 19 CMIP6 models, averaged over the SEA region for 1981–2010 [(modelled - observed)/observed × 100]. Numbers in the shaded boxes indicate relative error values. The MAE for each model is shown in the right-hand column, with models having the lowest errors highlighted in red.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5171746/v1/4b0a4cf815ea10a3c5b7a6de.png"},{"id":70937414,"identity":"7d03e4ed-ba13-4683-b053-a88a3d2827a0","added_by":"auto","created_at":"2024-12-09 11:15:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":233220,"visible":true,"origin":"","legend":"\u003cp\u003eTaylor diagrams for three extreme precipitation indices, displaying results for 19 CMIP6 models. The angular axis shows the model–observation spatial correlation coefficient, while the radial axis represents the normalized spatial standard deviation (RMS deviation) relative to observations. Each dot corresponds to a CMIP6 model, identified by its number.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5171746/v1/8cb956aa68a87d216fb7b7d6.png"},{"id":70939309,"identity":"3da3cd2f-6ca5-4b37-802c-ce43544a8930","added_by":"auto","created_at":"2024-12-09 11:31:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":85267,"visible":true,"origin":"","legend":"\u003cp\u003eIVS scores for CMIP6 models across three extreme precipitation indices over the SEA for 1981–2010.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5171746/v1/9687b6ab4dceded478c415fc.png"},{"id":70937416,"identity":"1bd36059-a33f-4247-abfd-a43f0404d091","added_by":"auto","created_at":"2024-12-09 11:15:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":72810,"visible":true,"origin":"","legend":"\u003cp\u003eScatter hue plots of MR scores for each model, showing spatial pattern (x-axis) from the Taylor diagram and temporal variability (y-axis) from the IVS, for the SEA region. Models in the green box, located in the top-right quadrant, represent the most skillful.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5171746/v1/55da5cf12daab7272aa42f54.png"},{"id":70937638,"identity":"668f2f24-0332-4d3a-8e61-d5ebf5e615e9","added_by":"auto","created_at":"2024-12-09 11:23:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":57977,"visible":true,"origin":"","legend":"\u003cp\u003eBox-and-whisker plots of relative errors for three extreme precipitation indices for better-performing_group and lower-performing_group over SEA. The boxes show the interquartile range (25th to 75th percentiles), with the median indicated by the horizontal line. The whiskers represent the range of relative errors for each group.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5171746/v1/f4190e052ec66d6c646c9cb7.png"},{"id":70937653,"identity":"007e08da-0bed-40f5-bd4e-9e1f87bc5dd4","added_by":"auto","created_at":"2024-12-09 11:23:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":715949,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of (left) observations and (center) model biases of the two ensembles for three extreme precipitation indices (rows) over the SEA region. Dotted areas in the center and right columns indicate regions with 95% confidence.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5171746/v1/213ec8ee757ac8632ffdf796.png"},{"id":70937641,"identity":"0405afbd-cd55-4bc9-8373-bdf6c40cf7ee","added_by":"auto","created_at":"2024-12-09 11:23:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":585458,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal distribution of the observation and model biases for the two ensembles across three extreme precipitation indices (rows) over the SEA region.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5171746/v1/defa6e80c5329ab9d2665bb2.png"},{"id":70939313,"identity":"cb6e1679-3870-40e4-a759-0f8bf68b5e41","added_by":"auto","created_at":"2024-12-09 11:31:09","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1105523,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of relative errors and model biases for three extreme precipitation indices (rows) over the SEA region. The dotted areas in the center and right columns indicate the 95% confidence level for observations and model ensembles.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-5171746/v1/a753f8fcfb5f61f779cadc37.png"},{"id":96105033,"identity":"17add1ef-27d7-47c0-a595-062096f5524c","added_by":"auto","created_at":"2025-11-17 16:07:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4202038,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5171746/v1/b0c366df-8e08-485e-a30c-ecd39ba30b7a.pdf"},{"id":70937412,"identity":"e1b84356-4186-49bc-a397-11006fb26f8a","added_by":"auto","created_at":"2024-12-09 11:15:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":21806,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-5171746/v1/a193b48cd9dc2604c5a38c54.docx"}],"financialInterests":"","formattedTitle":"Evaluating CMIP6 model performance of wet and dry spells by using novel climate rainfall indices over the Southeast Asia Region","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIt is well-documented that the world's climate has changed significantly, with a noticeable warming trend over the last century (Hollingsworth and Ingmann, 1999; IPCC, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This warming has accelerated the hydrological cycle and adjusted general circulation patterns, resulting in a marked increase in extreme precipitation events. The Southeast Asia (SEA) region, characterized by its complex climate dynamics, is particularly affected by these changes. This region is influenced by multiple monsoon systems, including the Indian summer monsoon and the western North Pacific summer monsoon (Chen and Yoon, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Feng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Oo, 2023). Additionally, the unique topography and intricate air\u0026ndash;sea interactions further complicate the spatiotemporal variations of precipitation and extreme precipitation in the SEA region (Buckley et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ge et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003es\u0026auml;nen et al., 2016). Given that extreme precipitation events significantly impact economic activities and human lives in the SEA region, understanding and quantifying these events is crucial for implementing effective measures to mitigate associated risks (Kim and Kug, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Ramarao et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhai and Zhuang, 2009).\u003c/p\u003e \u003cp\u003eGlobal climate models are essential tools for simulating current climate dynamics and projecting future climate scenarios (Orlowsky and Seneviratne, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Samuels et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Rao et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Despite advancements, these models often struggle to accurately capture the features of extreme precipitation due to the complexity of its temporal and spatial variability (Kim et al., 2018). A comprehensive analysis by Sillmann (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) revealed that Coupled Model Intercomparison Project Phase 5 (CMIP5) models generally overestimate extreme precipitation while underestimating consecutive wet days. This issue persists from CMIP3 through CMIP5, with significant uncertainties in simulating extreme precipitation in the tropics and subtropics (Sillmann, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Toreti et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Studies comparing models with satellite data have shown that many CMIP3 models underestimate the intensity of extreme precipitation in response to temperature, particularly in tropical regions (Allan et al., 2010; Donat et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Similarly, Raghavan et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that a subset of CMIP5 models struggled to reproduce the historical climate of Southeast Asia, and Li and Zhao (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) demonstrated that most CMIP5 models overestimate annual mean precipitation in the region. In China, although CMIP5 models capture the basic spatial and temporal characteristics of extreme precipitation in eastern China, they exhibit a wet bias in North China and a dry bias in South China (Jiang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ou et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sun et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent advancements in global climate modelling, particularly with the release of Phase 6 of the Coupled Model Intercomparison Project (CMIP6) models, offer enhanced resolution and improved parameterization for cloud microphysical processes and biogeochemical interactions (Eyring et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, 2019). These advancements are expected to result in better simulation accuracy for historical climate conditions (Mishra et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, while substantial research has focused on model evaluation and bias analysis in regions such as North America and Europe, there is a notable lack of similar studies for the SEA region (Misra and DiNapoli, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nguyen-Le et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tangang et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Quantitative analyses of extreme precipitation in the SEA region remain sparse (Raghavan et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003eb). It is crucial to assess how effectively each CMIP6 model simulates the spatial patterns and temporal variability of extreme precipitation in this region and to identify the most skillful models based on performance metrics. Establishing quantitative indicators will enable an evaluation of each model's ability to replicate observed precipitation features and comparing skillful and less skillful model ensembles will provide insights for improving extreme precipitation simulations in the SEA region.\u003c/p\u003e \u003cp\u003eThe precipitation and extreme precipitation mechanisms in the SEA region are complex and varied (Buckley et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hirabayashi et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang and Magnusdottir, 2005). Given these complexities, evaluating the performance of climate models in simulating extreme precipitation indices should be done separately for different subregions within the SEA. This approach offers a more accurate reference for model selection. Annual extreme precipitation indices are commonly used and widely accepted for such evaluations (Alexander and Arblaster, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ge et al., 2021; Zhu et al., 2020b). This study aims to quantitatively assess how well CMIP6 models simulate the spatial patterns and temporal variations of present-day precipitation and extreme precipitation in the SEA region using various skill score metrics on an annual basis. The study will classify models into different ensembles based on their performance across subregions, identifying the most and least skillful models. The final goal is to analyze the potential causes of biases by comparing these model ensembles with observational data.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area Description\u003c/h2\u003e \u003cp\u003eThe SEA region, located between 10\u0026deg;\u0026ndash;30\u0026deg;N and 80\u0026deg;\u0026ndash;120\u0026deg;E, encompasses parts of South Asia and Southeast Asia, including the coastal regions of Myanmar, Thailand, Bangladesh, and India. This region lies at the intersection of the Bay of Bengal and the South China Sea, making it a crucial area for understanding regional climate dynamics. The SEA's climate is strongly influenced by the Asian monsoon system, which governs seasonal variations in precipitation. Additionally, the region plays a significant role in modulating the retreat and advance of the Western Pacific Subtropical High in the Northern Hemisphere (Li et al., 2019).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven its tropical monsoon climate, the SEA region is prone to extreme precipitation events, which have been observed to intensify in recent years (Oo et al., 2023). The increasing trend in regional rainfall intensity highlights the importance of accurately simulating precipitation patterns for climate research. Consequently, evaluating how well CMIP6 models capture these climatic characteristics is critical for understanding the spatial and temporal variability of rainfall and extremes in the region. This evaluation will not only inform climate prediction efforts but also aid in the development of effective climate adaptation and mitigation strategies, especially in a region vulnerable to both monsoonal fluctuations and the broader impacts of climate change.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data source and acquisition\u003c/h2\u003e \u003cp\u003eDaily precipitation data from 19 CMIP6 models for the period 1981\u0026ndash;2010 were utilized to calculate three extreme precipitation indices PRCPTOT (total precipitation), SDII (simple daily intensity index), and CDD (consecutive dry days) over the SEA region, in order to identify the most accurate models for future regional climate prediction (You et al., 2018). Basic information for each model, including the model name, modelling center and atmospheric resolution, is provided in Supplementary S-1, with further details available online (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wcrp-climate.org/etccdi\u003c/span\u003e\u003cspan address=\"https://www.wcrp-climate.org/etccdi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn model evaluation, gridded datasets that incorporate homogenization and interpolation techniques are considered more reliable than station observations (Alexander et al., 2006). Accordingly, the daily gridded precipitation dataset from the Asian Precipitation\u0026ndash;Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) project was employed as the observational benchmark. This dataset, with a spatial resolution of 0.25\u0026deg; \u0026times; 0.25\u0026deg; for the period 1981\u0026ndash;2010, covers the region 15\u0026deg;S\u0026ndash;55\u0026deg;N and 60\u0026deg;E\u0026ndash;150\u0026deg;E (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://aphrodite.st.hirosaki-u.ac.jp/download/\u003c/span\u003e\u003cspan address=\"http://aphrodite.st.hirosaki-u.ac.jp/download/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (Yatagai et al., 2012). It was found that the APHRODITE data underestimated the precipitation amount and overestimated the number of rain days for the entire study region (Ji et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo ensure consistency, the extreme precipitation indices calculated from both observational data and model outputs were first computed on their respective native grids and subsequently interpolated to a common 1\u0026deg; \u0026times; 1\u0026deg; latitude-longitude grid using bilinear interpolation, following the methods of Kharin et al. (2013), Oo et al. (2023), and Ou et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Extreme Precipitation Indices\u003c/h2\u003e \u003cp\u003eFollowing the definitions established by the Expert Team on Climate Change Detection and Indices (ETCCDI; Zhang et al., 2011), this study employed several extreme precipitation indices (Supplementary S-2) to evaluate the SEA region. The selected indices include total wet-day precipitation (PRCPTOT), the simple daily intensity index (SDII), and consecutive dry days (CDD) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These indices capture fundamental aspects of precipitation and extremes and have been widely applied in previous studies (Alexander and Arblaster, 2009; Jiang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Mie et al., 2022; Oo et al., 2023; Sillmann et al., 2013). Additional details on these extreme precipitation indices can be accessed via the ETCCDI website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://etccdi.pacificclimate.org/indices.shtml\u003c/span\u003e\u003cspan address=\"http://etccdi.pacificclimate.org/indices.shtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Total Wet-Day Precipitation (PRCPTOT)\u003c/h2\u003e \u003cp\u003eTotal wet-day precipitation (PRCPTOT) refers to the cumulative precipitation on days when the recorded precipitation is equal to or greater than 1 mm. This index offers a comprehensive measure of the total precipitation that occurs during wet days over a defined period.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:PRCPTO{T}_{j}={\\sum\\:}_{n=1}^{N}{RR}_{ij}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{RR}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is the daily precipitation amount on day i in period j, and N represents the number of wet days in period j.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Simple Daily Intensity Index (SDII)\u003c/h2\u003e \u003cp\u003eThe Simple Daily Intensity Index (SDII) quantifies the average intensity of precipitation on wet days. It is determined by dividing the total precipitation amount by the number of wet days over a given period.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{SDII}_{j}=\\frac{{\\sum\\:}_{w=1}^{w}{RR}_{wj}}{W}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{RR}_{wj}\\)\u003c/span\u003e\u003c/span\u003e is the daily precipitation amount on a wet day \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:w(RR\\ge\\:1.0mm)\\)\u003c/span\u003e\u003c/span\u003e in period j. W represents the number of wet days in j.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Consecutive Dry Days (CDD)\u003c/h2\u003e \u003cp\u003eConsecutive Dry Days (CDD) represent the maximum number of consecutive days with daily precipitation less than 1 mm. This index serves as an indicator of drought conditions.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:CDD=\\text{m}\\text{a}\\text{x}(consercutive\\:days\\:with\\:{RR}_{ij}\u0026lt;1mm)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIt is important to note that the calculation of these three extreme precipitation indices PRCPTOT, SDII, and CDD for a given period (month, year, or rainy season) was based on daily data from each year within the 1981\u0026ndash;2010 timeframe.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Model Performance Metrics\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Taylor Diagram\u003c/h2\u003e \u003cp\u003eWe utilized the Taylor diagram to assess the models' performance in reproducing the observed spatial patterns of extreme precipitation indices in the SEA region. The Taylor diagram facilitates a succinct comparison between model simulations and observations by incorporating the pattern correlation coefficient, centered pattern root-mean-square (RMS) difference, and the ratio of the spatial standard deviations of modelled to observed values (Taylor, 2001). A model is considered more skillful in replicating the spatial patterns of observed extreme precipitation indices if the centered RMS difference is closer to 0, and both the spatial correlation and the ratio of spatial standard deviations approach 1. This approach provides both a visual and quantitative evaluation of the models' ability to capture the spatial characteristics of extreme precipitation events in the SEA region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Interannual variability skill score (IVS)\u003c/h2\u003e \u003cp\u003e \u003cdiv id=\"Equd\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:IVS=(\\frac{{STM}_{m}}{{STD}_{o}}-\\frac{{STD}_{o}}{{STD}_{m}}{)}^{2}$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{STD}_{m}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{STD}_{o}\\)\u003c/span\u003e\u003c/span\u003e are the interannual standard deviations of the model simulations and observations, respectively. The calculations of STDm and STDo are based on yearly data (Jiang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e;Ren et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A smaller IVS value implies better model simulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Comprehensive Rating Metrics\u003c/h2\u003e \u003cp\u003eBased on the Taylor diagram and Interannual Variability Skill Score (IVS) values, each extreme precipitation index was employed to rank the models according to their spatial and temporal simulation capabilities. A comprehensive ranking metric (MR; Jiang et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) was computed separately for spatial and temporal simulation capabilities. The MR is defined as follows:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:MR=1-\\frac{1}{nm}\\sum\\:_{i=1}^{n}{rank}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere m is the number of models, n is the number of extreme precipitation indices, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{rank}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the model rank for the ith extreme precipitation index. Here, the maximum value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{rank}_{i}\\)\u003c/span\u003e\u003c/span\u003e is 40, and the minimum value is 1. The better the model performance, the smaller the value of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{rank}_{i}\\)\u003c/span\u003e\u003c/span\u003e. Therefore, the closer MR is to 1, the better the model performance is.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Regional Averages of the Four Extreme Precipitation Indices Over the Subregions\u003c/h2\u003e \u003cp\u003eTo evaluate the models' ability to simulate extreme precipitation indices in the SEA region, relative errors between the observed data and model outputs were calculated for each subregion.\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:Relative\\:error=\\frac{Modeled-observed}{observed}\\times\\:100$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays a color-coded \"portrait diagram\" illustrating the relative errors for each extreme precipitation index across all models. In this diagram, both the color shading and numerical values within each box represent the magnitude of the relative error.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Model evaluation\u003c/h2\u003e \u003cp\u003eThe performance of each model in reproducing both the spatial patterns and temporal variability of the extreme precipitation indices in the SEA region was evaluated independently.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3 Results and discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Important features observed\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Overestimation in Precipitation Metrics\u003c/h2\u003e \u003cp\u003eMost CMIP6 models tend to overestimate annual precipitation, annual extreme precipitation, and precipitation intensity in the SEA region, as indicated by positive relative errors for PRCPTOT, CDD, and SDII in the majority of models. These findings are consistent with previous research (Zhu et al., 2020a).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e displays the relative errors for three extreme precipitation indices\u0026mdash;PRCPTOT, SDII, and CDD\u0026mdash;simulated by 19 CMIP6 models, averaged over a specified region for the period 1981\u0026ndash;2010. The Mean Absolute Error (MAE) for each model is also shown in the final column. This analysis reveals the strengths and weaknesses of the CMIP6 models in simulating extreme precipitation events, which is critical for understanding and predicting climate impacts in the region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Variability in Relative Errors\u003c/h2\u003e \u003cp\u003eThe relative errors for PRCPTOT varied significantly among the models. INM-CM4-8 exhibited the largest positive relative error at 18%, while KIOST-ESM had the most pronounced negative relative error at -24%. Most models tended to underestimate PRCPTOT, as indicated by the prevalence of negative errors.\u003c/p\u003e \u003cp\u003eFor SDII, the models consistently underestimated precipitation intensity, with errors ranging from \u0026minus;\u0026thinsp;10% (KACE-1-0-G) to -41% (KIOST-ESM). KIOST-ESM showed the most substantial underestimation, reflecting a notable discrepancy in the simulation of daily precipitation intensity.\u003c/p\u003e \u003cp\u003eRegarding CDD, the models showed a wide range of errors, from \u0026minus;\u0026thinsp;53% (INM-CM4-8) to 92% (MPI-ESM1-2-HR). INM-CM4-8 and INM-CM5-0 significantly underestimated CDD, while MPI-ESM1-2-HR and MPI-ESM1-2-LR overestimated it by a large margin.\u003c/p\u003e \u003cp\u003eThe Mean Absolute Error (MAE), summarizing the overall error for each model, ranged from 8.7 (ACCESS-CM2) to 46 (KIOST-ESM). Models with lower MAE values, such as ACCESS-CM2 (8.7) and NESM3 (9.8), demonstrated better performance in simulating extreme precipitation indices. In contrast, KIOST-ESM (46) and INM-CM4-8 (36) exhibited higher MAE, indicating greater overall errors in simulating the observed data.\u003c/p\u003e \u003cp\u003eThe ACCESS-CM2, NESM3, and BCC-CSM2-MR models exhibited relatively lower errors across the indices, indicating they are more reliable for simulating extreme precipitation events in the SEA region. In contrast, KIOST-ESM and INM-CM4-8 showed higher errors, suggesting these models require improvement for more accurate extreme precipitation simulations. The consistent underestimation of SDII by most models highlights a general difficulty in capturing daily precipitation intensity accurately. For CDD, the models displayed substantial variability, with some significantly underestimating or overestimating dry spells, indicating challenges in accurately simulating prolonged dry periods.\u003c/p\u003e \u003cp\u003eOverall, most CMIP6 models performed relatively well in reproducing the extreme precipitation indices, with smaller relative errors. To better assess model performance, the mean absolute error (MAE) of the relative errors (shown in the right-hand column of Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) was calculated for each model. Based on MAEs, models such as ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, MIROC6, and NESM3 demonstrated the highest skill in the study area, with relative errors below 15%. However, most models still performed moderately, with errors below 30%, except for INM-CM4-8, MPI-ESM1-2-LR, MPI-ESM1-2-HR, and KIOST-ESM, which exhibited higher relative errors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Taylor Diagram\u003c/h2\u003e \u003cp\u003eThe Taylor diagrams for the three extreme precipitation indices (PRCPTOT, CDD, and SDII) using 19 CMIP6 models provide a comprehensive assessment of model performance by comparing them with observations. These diagrams focus on three key metrics: the correlation coefficient, standard deviation, and root mean square deviation (RMSD). They offer insights into the strengths and weaknesses of the models in simulating various aspects of extreme precipitation events.\u003c/p\u003e \u003cp\u003eMost models showed moderate to high correlation with observations for PRCPTOT, with correlation coefficients generally ranging from 0.4 to 0.8. This indicates that the models capture the spatial patterns of total precipitation relatively well.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe standard deviation for most models was close to 1, suggesting that they simulated the variability of total precipitation accurately. Models such as ACCESS-CM2 and BCC-CSM2-MR demonstrated better performance, with relatively high correlation and standard deviations close to 1, indicating a strong match with observations.\u003c/p\u003e \u003cp\u003eFor CDD, the models generally exhibited lower correlation coefficients compared to PRCPTOT, with values mostly between 0.1 and 0.5. This suggests that the models struggle to capture the spatial patterns of dry days accurately. The standard deviation for many models deviated from 1, indicating discrepancies in the simulation of dry day variability, with some models either overestimating or underestimating this variability. For instance, while MPI-ESM1-2-LR and GFDL-ESM4 exhibited higher correlation, they also showed significant deviations in standard deviation, suggesting a trade-off between spatial pattern accuracy and amplitude consistency.\u003c/p\u003e \u003cp\u003eFor SDII, the models displayed moderate to high correlation coefficients, generally ranging from 0.4 to 0.7, indicating that they reasonably capture daily precipitation intensity. The standard deviation for SDII was close to 1 in many models, suggesting that they accurately simulated the variability in daily precipitation intensity. CanESM5 and MRI-ESM2-0 showed particularly strong performance, with relatively high correlations and standard deviations close to 1, indicating more accurate simulations of daily precipitation intensity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe PRCPTOT index was generally better captured by the models in terms of both correlation and standard deviation, indicating stronger overall performance in simulating total precipitation. The CDD index presented the greatest challenge, with models showing lower correlations and higher variability in standard deviation, reflecting difficulties in accurately simulating dry spells. The SDII index was moderately well-simulated, with several models exhibiting high correlation and accurate variability, though some still showed deviations.\u003c/p\u003e \u003cp\u003eACCESS-CM2 and BCC-CSM2-MR consistently performed well across multiple indices, highlighting their robustness in simulating extreme precipitation events. Models like MPI-ESM1-2-HR and GFDL-ESM4 performed well in certain indices but exhibited trade-offs in others, such as overestimating or underestimating variability while maintaining reasonable correlation. KIOST-ESM and INM-CM4-8, which showed higher errors in previous analyses, also demonstrated poorer performance in the Taylor diagrams, particularly in terms of correlation for indices like CDD.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Evaluation of Spatial Variation\u003c/h2\u003e \u003cp\u003eThe climatological spatial distributions of the three extreme precipitation indices, based on observations and 19 CMIP6 models for the period 1981\u0026ndash;2010, are presented. The observed spatial patterns are generally consistent across the indices, with large areas of high annual precipitation, extreme precipitation, and precipitation intensity concentrated along the coastal regions and key areas of the SEA region. However, the highest numbers of consecutive dry days (CDD) are confined to specific areas. Most models struggle to capture the observed spatial patterns of all four extreme precipitation indices.\u003c/p\u003e \u003cp\u003eTo provide a concise summary of the models' performance in simulating the spatial patterns of these indices, Taylor diagrams (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) were used. The spatial correlation coefficients for most CMIP6 models range between 0.3 and 0.9 for SDII in the SEA region. However, for R95p and CDD, a few models have spatial correlation coefficients below 0.3 (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, c), and nearly all models perform poorly in simulating PRCPTOT (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). This indicates that CMIP6 models have limited skill in simulating extreme precipitation indices, except for SDII in the SEA region.\u003c/p\u003e \u003cp\u003eIn certain subregions within the SEA, the spatial correlation coefficients for CDD are greater than 0.3 for all CMIP6 models (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), while three-quarters of the models fail to simulate the spatial patterns of PRCPTOT and SDII, with spatial correlation coefficients below 0.3 (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, b, c). These findings suggest that CMIP6 models only simulate the spatial distribution of CDD effectively in certain subregions. Most models show a ratio of variance less than 1 for PRCPTOT and SDII (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;c), while fewer models show a ratio of variance approaching 1 for CDD, indicating that most models underestimate the spatial variability of annual precipitation, extreme precipitation, and precipitation intensity, but overestimate the spatial variability of consecutive dry days.\u003c/p\u003e \u003cp\u003eFor other subregions, most models exhibit a variance ratio of less than 1 for PRCPTOT and SDII, while the variance ratio is generally greater than 1 for CDD. Only a few models show a variance ratio for PRCPTOT approaching 1. This suggests that most models underestimate the spatial variation of extreme precipitation and precipitation intensity but overestimate the variability of consecutive dry days in these subregions.\u003c/p\u003e \u003cp\u003eAcross both the SEA region and its subregions, the majority of models show centered normalized RMS differences (indicated by grey solid lines) for PRCPTOT that are larger than 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), indicating substantial bias in simulating annual extreme precipitation. The relatively concentrated distributions in the Taylor diagrams suggest a relatively small inter-model spread for all indices over the SEA region (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea\u0026ndash;c).\u003c/p\u003e \u003cp\u003eOverall, the CMIP6 models perform relatively poorly in simulating the spatial patterns of extreme precipitation indices in the SEA region. Among the indices, SDII is better simulated in some subregions, while CDD is well captured in other areas of the SEA region. In general, the spatial variations of extreme precipitation indices are more accurately simulated in certain subregions than in others.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Evaluation of Interannual Variability\u003c/h2\u003e \u003cp\u003eIn addition to spatial patterns, evaluating the models' ability to simulate the temporal variability of extreme precipitation indices is crucial for comprehensive model assessment. To this end, the Interannual Variability Skill (IVS) score was employed to quantify the similarity between the interannual variability of the modelled and observed extreme precipitation indices. IVS scores for the three extreme precipitation indices, averaged over the subregions of the SEA region, were calculated for the period 1981\u0026ndash;2010 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe interannual variability of the indices was better simulated in certain subregions, as indicated by the narrower range of IVS values. For example, the IVS scores for PRCPTOT, CDD, and SDII ranged from 0 to 1 in some subregions, while CDD exhibited more variability, ranging from 0 to 1.7 in other parts of the SEA region. In specific subregions, the interannual variability of SDII was well captured by most CMIP6 models, with all models showing good performance in simulating the observed interannual variability of SDII.\u003c/p\u003e \u003cp\u003eModels such as BCC-CSM2-MR and ACCESS-ESM1-5 stood out with relatively higher IVS values, indicating superior performance in simulating the interannual variability of total precipitation. In contrast, many other models exhibited lower IVS scores, suggesting difficulties in accurately capturing this variability. Notably, GFDL-ESM4, MRI-ESM2-0, and MPI-ESM1-2-LR achieved higher IVS scores for CDD, with scores exceeding 1.0, indicating strong performance in simulating the variability of dry days. However, a significant number of models showed lower or negligible IVS scores for CDD, indicating poor performance in capturing the interannual variability of consecutive dry days.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor SDII, models such as ACCESS-CM2, BCC-CSM2-MR, and MIROC6 displayed moderate IVS scores, reflecting decent performance in simulating the interannual variability of daily precipitation intensity. Similar to PRCPTOT, most models struggled to simulate the year-to-year variability of SDII, as evidenced by lower IVS scores. GFDL-ESM4 and MRI-ESM2-0 particularly excelled in simulating the interannual variability of CDD, demonstrating strength in this area. Models like ACCESS-CM2 and BCC-CSM2-MR showed consistent performance across multiple indices, highlighting their robustness in capturing the interannual variability of extreme precipitation events in the region.\u003c/p\u003e \u003cp\u003eHowever, many models exhibited lower IVS scores for both PRCPTOT and SDII, underscoring the challenge these models face in accurately simulating interannual variability.\u003c/p\u003e \u003cp\u003eThe IVS analysis reveals significant variations in model performance when simulating the interannual variability of extreme precipitation indices. While models such as GFDL-ESM4 and MRI-ESM2-0 excel in specific indices (e.g., CDD), others like ACCESS-CM2 and BCC-CSM2-MR perform consistently well across all indices. Nonetheless, many models show limitations, particularly in simulating the variability of total precipitation and daily precipitation intensity, highlighting areas for further improvement in climate modelling.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Overall Model Ranking\u003c/h2\u003e \u003cp\u003eDue to inconsistencies between model rankings based on spatial patterns and interannual variability of extreme precipitation indices, it is crucial to consider both aspects in the evaluation process. This study highlights that the alignment between spatial and temporal simulations varies across different subregions of the SEA.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo address this, models were categorized into two ensembles based on their performance in both spatial and temporal dimensions. The criteria were: (i) Models with mean rankings (MRs) above 0.6 for both spatial and temporal dimensions were classified as part of the \"Good_group,\" while (ii) Models with MRs below 0.6 for both dimensions were classified as part of the \"Bad_group.\"\u003c/p\u003e \u003cp\u003eAccording to these criteria, six models - ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, MIROC6, NorESM2-LM, and NorESM2-MM - were identified as the most skillful, demonstrating strong performance in both spatial and temporal simulations. Conversely, four models\u0026mdash;EC-Earth3, EC-Earth3-Veg, GFDL-ESM4, and MPI-ESM1-2-LR - were classified as less skillful, showing deficiencies in both aspects.\u003c/p\u003e \u003cp\u003eGenerally, the model evaluation for the SEA region identified a group of six high-performing models and four less effective models. This classification provides a clearer perspective on model performance and aids in selecting the most suitable models for further research and applications related to extreme precipitation in the SEA region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Performance of Optimal Models\u003c/h2\u003e \u003cp\u003eIn the previous section, we identified the most and least skillful models for simulating extreme precipitation indices in the SEA region. This section compares the performance of the Good_group and Bad_group across each extreme precipitation index for the period 1981\u0026ndash;2010, based on relative errors presented in box-and-whisker plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). For the first subregion, simulations of PRCPTOT and SDII by the better-performing group (denoted as Good) demonstrated notable improvements compared to the lower-performing_group (denoted as bad), as indicated by median values closer to zero, smaller ranges, and reduced interquartile ranges. The Good_group also showed some improvement in simulating CDD, though to a lesser extent.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e further illustrates the spatial patterns and biases of the two model ensembles (Good_group and Bad_group) for the four extreme precipitation indices across the SEA region. The model biases represent the differences between the two model ensembles and observations for the respective subregions.\u003c/p\u003e \u003cp\u003eThe observed climatological spatial distributions are consistent for PRCPTOT (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea), CDD (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb), and SDII (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec), with high values of annual precipitation and extreme precipitation concentrated along coastal regions and other key areas of the SEA region, while lower values are found in specific areas. High CDD values are primarily located in certain regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the first subregion, the better-performing Good group exhibited smaller biases compared to the lower-performing Bad group for PRCPTOT, CDD, and SDII, particularly in specific regions. However, the Good_group showed less annual total precipitation and extreme precipitation in certain areas of the first subregion compared to the Bad_group, which exhibited a wet bias. Additionally, the Good_group significantly reduced the wet bias over most of the second subregion compared to the Bad_group. For example, the mean absolute errors for CDD in the Bad_group reached up to 80 days, while they were less than 80 days in the Good_group for the second subregion.\u003c/p\u003e \u003cp\u003eOverall, the Good_group models demonstrated significantly better performance in simulating extreme precipitation indices, exhibiting smaller biases and more accurate spatial and temporal representations in the SEA region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe historical interannual cycles for PRCPTOT, CDD, and SDII, averaged over the SEA region, are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The red lines represent the ensemble means of all CMIP6 models, Good_group, and Bad_group, while the black dotted lines indicate the observed interannual cycles. All ensemble means capture the basic characteristics of the interannual cycles for PRCPTOT, CDD, and SDII. However, for the first subregion, the PRCPTOT (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea), CDD (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eb), and SDII (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ec) simulated by the Good_group ensemble mean is more realistic compared to those of the other two ensemble means.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe figure depicts the spatial distribution of relative errors and biases for two model ensembles, \"Good\" and \"Bad,\" across three extreme precipitation indices - PrcpTOT (total precipitation), CDD (consecutive dry days), and SDII (simple daily intensity index) - over the SEA region. For the PrcpTOT index, the \"Good\" ensemble (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.a) showed a mix of positive and negative biases, with significant positive biases in northeastern India and Myanmar, and negative biases in parts of northern India. In contrast, the \"Bad\" ensemble (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.b) displayed an overall increase in positive biases, particularly in the northern Bay of Bengal and extending into Myanmar and Thailand.\u003c/p\u003e \u003cp\u003eRegarding the CDD index, the \"Good\" ensemble (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.c) revealed negative biases across most regions, indicating fewer consecutive dry days than observed. Conversely, the \"Bad\" ensemble (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.d) exhibited widespread positive biases, reflecting an overestimation of consecutive dry days, especially in central Myanmar and northern Southeast Asia.\u003c/p\u003e \u003cp\u003eFor the SDII index, the \"Good\" ensemble (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.e) showed moderate positive biases across the region, with notable increases along coastal Myanmar. The \"Bad\" ensemble (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.f) exacerbated these biases, particularly in the northeastern areas, indicating an overestimation of daily precipitation intensity. The dotted areas in the center and right columns represent regions where the differences are statistically significant at the 95% confidence level, highlighting the more pronounced errors in the \"Bad\" ensemble compared to the \"Good\" ensemble.\u003c/p\u003e \u003cp\u003eIn analysing water vapor transport and convergence, the \"Good\" group models generally showed smaller biases in water vapor convergence compared to the \"Bad\" group. They also better simulated the spatial variation and interannual variability of specific humidity and vertical velocity during the rainy season, evidenced by larger pattern correlation coefficients and smaller centered normalized RMS differences and IVS skill scores. This leads to more accurate simulations of precipitation and extreme precipitation.\u003c/p\u003e \u003cp\u003eWhile higher resolution alone doesn\u0026rsquo;t guarantee better skill, the \u0026ldquo;Good\u0026rdquo; models outperform the \u0026ldquo;Bad\u0026rdquo; ones due to smaller biases in water vapor convergence and better simulations of key processes like spatial variation, interannual variability of specific humidity, and vertical velocity during the rainy season. These strengths are evident from higher pattern correlation coefficients, smaller normalized RMS differences, and improved IVS skill scores.\u003c/p\u003e \u003cp\u003eInterestingly, while higher resolution can enhance the simulation of fine-scale processes (as noted by Diffenbaugh et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, it\u0026rsquo;s not the main determinant of model skill in the SEA region. Many \u0026ldquo;Good\u0026rdquo; models achieve better accuracy despite not having high resolution, indicating that other factors, such as parameterization schemes and model dynamics, play a crucial role.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Conclusion and recommendations","content":"\u003cp\u003eThis study provides a comprehensive evaluation of CMIP6 models in simulating extreme precipitation indices over the SEA region. The analysis differentiated between two model ensembles, \"Good\" and \"Bad,\" based on their ability to accurately represent both spatial patterns and temporal variability of extreme precipitation events. The findings reveal that models within the \"Good\" ensemble generally exhibited superior performance compared to those in the \"Bad\" ensemble. Specifically, the \"Good\" models showed smaller biases and more accurate spatial representations for indices such as total precipitation (PrcpTOT), consecutive dry days (CDD), and daily precipitation intensity (SDII). Additionally, these models demonstrated a better alignment with observed interannual variability, particularly for CDD and SDII. Conversely, the \"Bad\" models displayed substantial discrepancies in both spatial patterns and temporal variability, with higher relative errors and less precise simulations. The study also highlighted that, despite advancements in model resolution, horizontal resolution did not appear to be a primary factor in determining model performance in simulating extreme precipitation. This suggests that other elements, such as model physics and parameterization, play a more critical role in achieving accurate simulations.\u003c/p\u003e \u003cp\u003eTo enhance the accuracy of extreme precipitation simulations, several steps should be considered. Firstly, there is a need to refine models to better capture extreme precipitation events, with particular emphasis on improving the simulation of consecutive dry days (CDD) and daily precipitation intensity (SDII). Enhancing the representation of water vapor transport and moisture conditions is also crucial, as accurate simulation of these factors contributes to more realistic precipitation predictions. When selecting models for future studies or practical applications related to extreme precipitation in the SEA region, it is advisable to prioritize those models that have demonstrated consistent strength across both spatial and temporal dimensions. The \"Good\" models identified in this study should be favoured for their overall robustness and accuracy.\u003c/p\u003e \u003cp\u003eFurther research should focus on understanding how different model physics and parameterizations affect the simulation of extreme precipitation. This understanding can guide refinements to models, enabling them to better capture fine-scale climate processes. Additionally, while higher resolution models did not universally outperform lower resolution models in this study, ongoing evaluation of the impact of resolution on simulation accuracy remains important. Continued investigation into the role of resolution can help optimize model configurations for various contexts. By following these recommendations, future climate modelling efforts can improve the precision of extreme precipitation predictions and enhance the understanding of climate variability in the SEA region.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflict of Interest:\u003c/h2\u003e\n\u003cp\u003eThe authors have no competing interests to declare.\u003c/p\u003e\n\u003ch2\u003eData Statement:\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and utilized in this study are available from the corresponding author upon request.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eThis research is supported by National Natural Science Foundation of China (No. 32361143869).\u003c/p\u003e\n\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e\n\u003cp\u003eThet Mar Soe: Conceptualization, Data curation, Methodology, Writing-Original draft preparation, Visualization. Abraham Okrah: Conceptualization, Data curation, Methodology, Writing-Original draft preparation, Kyaw Than Oo: Conceptualization, Data curation, Methodology, Visualization Reviewing and Editing. Ebaju Gerverse Kamukama: Data curation, Methodology, Writing-Original draft preparation, Reviewing and Editing. Fangmin Zhang: Conceptualization, Data Curation, Methodology, Supervision, Reviewing and editing.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement:\u003c/h2\u003e\n\u003cp\u003eThe first author [Thet Mar Soe], expresses sincere gratitude to the World Meteorological Organization-Chinese Scholarship Council and Nanjing University of Information Science and Technology, Nanjing-China, for their support in sponsoring her Master\u0026apos;s studies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlexander LV, Arblaster JM (2017) Assessing changes in extreme precipitation: A global and regional perspective. 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Part I: Model evaluation. J Clim 28(21):8603\u0026ndash;8619. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1175/JCLI-D-15-0099.1\u003c/span\u003e\u003cspan address=\"10.1175/JCLI-D-15-0099.1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra V, Aadhar S, Mahto SS (2021) Anthropogenic warming and intraseasonal summer monsoon variability amplify the risk of future flash droughts in India. Npj Clim Atmospheric Sci 4(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41612-020-00158-3\u003c/span\u003e\u003cspan address=\"10.1038/s41612-020-00158-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRen YY, Ren GY, Sun XB, Shrestha AB, You QL, Zhan YJ, Rajbhandari R, Zhang PF, Wen KM (2017) Observed changes in surface air temperature and precipitation in the Hindu Kush Himalayan region over the last 100-plus years. Adv Clim Change Res 8(3):148\u0026ndash;156. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/J.ACCRE.2017.08.001\u003c/span\u003e\u003cspan address=\"10.1016/J.ACCRE.2017.08.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Climate modelling, CMIP6 models, Southeast Asia, Extreme precipitation, Model evaluation","lastPublishedDoi":"10.21203/rs.3.rs-5171746/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5171746/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding and accurately predicting precipitation events is crucial due to their significant impacts on human lives and economies, especially in the Southeast Asia (SEA) region. This study evaluates the performance of CMIP6 models in simulating precipitation indices, focusing on both spatial patterns and temporal variations from 1981 to 2010. Models were classified into two groups: the 'Good_group,' which demonstrated robust performance, and the 'Bad_group,' which exhibited notable biases. The Good_group comprising ACCESS-CM2, ACCESS-ESM1-5, BCC-CSM2-MR, MIROC6, NorESM2-LM, and NorESM2-MM consistently performed better in simulating total precipitation (PRCPTOT), consecutive dry days (CDD), and daily intensity (SDII). These models showed smaller biases and more accurate representations of spatial patterns and temporal variability in the SEA region. Conversely, the Bad_group including EC-Earth3, EC-Earth3-Veg, GFDL-ESM4, and MPI-ESM1-2-LR exhibited significant biases and poorer performance. Specifically, Good_group models provided a more realistic simulation of PRCPTOT and SDII with reduced biases, whereas Bad_group models showed larger errors, especially in northeastern India and Myanmar. For CDD, Good_group models estimated fewer consecutive dry days than observed, while Bad_group models overestimated them. Despite advancements in CMIP6 models, including higher resolutions and improved parameterizations, challenges persist in accurately simulating wet spells dynamics in the complex SEA region. This study identifies the most skilful models and areas for improvement, offering valuable insights for model selection and enhancing climate projections and adaptation strategies in the SEA region.\u003c/p\u003e","manuscriptTitle":"Evaluating CMIP6 model performance of wet and dry spells by using novel climate rainfall indices over the Southeast Asia Region","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-09 11:15:03","doi":"10.21203/rs.3.rs-5171746/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-12-06T10:02:21+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-12-06T01:18:18+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-05T11:43:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climate Dynamics","date":"2024-12-04T20:00:49+00:00","index":"","fulltext":""},{"type":"decision","content":"Major Revision","date":"2024-12-02T21:18:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"52b7e6dd-4107-4ac6-bdfd-272542701ac6","owner":[],"postedDate":"December 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T16:01:34+00:00","versionOfRecord":{"articleIdentity":"rs-5171746","link":"https://doi.org/10.1007/s00382-025-07938-8","journal":{"identity":"climate-dynamics","isVorOnly":false,"title":"Climate Dynamics"},"publishedOn":"2025-11-10 15:57:53","publishedOnDateReadable":"November 10th, 2025"},"versionCreatedAt":"2024-12-09 11:15:03","video":"","vorDoi":"10.1007/s00382-025-07938-8","vorDoiUrl":"https://doi.org/10.1007/s00382-025-07938-8","workflowStages":[]},"version":"v1","identity":"rs-5171746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5171746","identity":"rs-5171746","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}