Past assessments of coupled climate models have indicated that precipitation extremes are expected to intensify over Southeast Asia (SEA) under the global warming. Here, we use outputs from 15 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to evaluate projected changes in precipitation extremes for SEA at the end of the 21st century. The results suggest that CMIP6 multi-model ensemble medians show better performances in characterizing precipitation extremes than individual models. Projected changes in precipitation extremes linked to rising greenhouse gas (GHG) emissions (represented by the latest proposed Shared Socioeconomic Pathways) increase significantly over the Indochina Peninsula and the Maritime Continent. Substantial changes in the number of very heavy precipitation days (R20mm) and the intensity of daily precipitation (SDII) indicate that such locally heavy rainfall is likely to occur over a short time and that more precipitation extremes over SEA are probable in a warmer future. This is consistent with projections from the Coordinated Regional Downscaling Experiment and CMIP5 models. The present study reveals the high sensitivity of the precipitation extremes over SEA, and highlights the importance of constrained anthropogenic GHG emissions in an ambitious mitigation scenario.

Guided by the target of the Paris Agreement of 2015, it is fundamental to identify regional climate responses to global warming of different magnitudes for Southeast Asia (SEA), a tropical region where human society is particularly vulnerable to climate change. Projected changes in indices characterizing precipitation extremes of the 1.5 °C and 2 °C global warming levels (GWLs) exceeding pre-industrial conditions are analyzed, comparing the reference period (1976-2005) with an ensemble of CORDEX simulations. The results show that projected changes in precipitation extreme indices are significantly amplified over the Indochina Peninsula and the Maritime Continent at both GWLs. The increases of precipitation extremes are essentially affected by enhanced convective precipitation. The number of wet and extremely wet days is increasing more abruptly than both the total and daily average precipitation of all wet days, emphasizing the critical risks linked with extreme precipitation. Additionally, significant changes can also be observed between the GWLs of 1.5 °C and 2 °C, especially over the Maritime Continent, suggesting the high sensitivity of precipitation extremes to the additional 0.5 °C GWL increase. The present study reveals the potential influence of both 1.5 °C and 2 °C GWLs on regional precipitation over SEA, highlights the importance of restricting mean global warming to 1.5 °C above pre-industrial conditions and provides essential information on manageable climate adaptation and mitigation strategies for the developing countries in SEA.

The reliable Subseasonal‐to‐Seasonal (S2S) forecast of precipitation, particularly extreme precipitation, is critical for disaster prevention and mitigation, which however remains a great challenge for mission agencies and research communities. In this study, a deep learning method based on U‐Net with additional atmospheric factor forecasts included is proposed to improve S2S quantitative forecasts of summer precipitation over Southern China. The weighted loss function integrated by mean square error and threat score is introduced to capture extreme precipitation more precisely. Generally, the U‐Net model shows promising results in both general statistics and extreme events. Predictor importance analyses show that the U‐Net forecast skills at the 1‐week lead time mainly arise from synchronous precipitation forecasts, but the contributions made by atmospheric factor forecasts rise rapidly with increasing lead times. Therefore, the channel combining numerical weather prediction model and deep learning framework is demonstrated promising in S2S precipitation forecasts.

Temperature extremes have increased during the past several decades and are expected to intensify under current rapid global warming over Southeast Asia (SEA). Exposure to rising temperatures in highly vulnerable regions affects populations, ecosystems, and other elements that may suffer potential losses. Here, we evaluate changes in temperature extremes and future population exposure over SEA at global warming levels (GWLs) of 2.0 °C and 3.0 °C using outputs from the Coupled Model Intercomparison Project Phase 6. Results indicate that temperature extreme indices are projected to increase over SEA at both GWLs, with more significant magnitudes at 3.0 °C. However, daily temperature ranges show a decrease. The substantial increase in total SEA population exposure to heat extremes from 730 million person–days at 2.0 °C GWL to 1200 million person–days at 3.0 °C GWL is mostly contributed by the climate change component, accounting for 48%. In addition, if global warming is restricted well below 2.0 °C, the avoided impacts in population exposure are prominent for most regions over SEA with the largest mitigation in the Philippines. Aggregate population exposure to impacts is decreased by approximately 39% at 2.0 °C GWL, while the interaction component effect, which is associated with increased population and climate change, would decrease by 53%. This indicates serious consequences for growing populations concurrent with global warming impacts if the current fossil-fueled development pathway is adhered to. The present study estimates the risks of increased temperature extremes and population exposure in a warmer future, and further emphasizes the necessity and urgency of implementing climate adaptation and mitigation strategies in SEA.

We analyzed 24-h accumulated precipitation forecasts over the 4-month period from 1 May to 31 August 2013 over an area located in East Asia covering the region 15.05°–58.95°N, 70.15°–139.95°E generated with the ensemble prediction systems (EPS) from ECMWF, NCEP, UKMO, JMA, and CMA contained in the TIGGE dataset. The forecasts are first evaluated with the Method for Object-Based Diagnostic Evaluation (MODE). Then a multimodel ensemble (MME) forecast technique that is based on weights derived from object-based scores is investigated and compared with the equally weighted MME and the traditional gridpoint-based MME forecast using weights derived from the point-to-point metric, mean absolute error (MAE). The object-based evaluation revealed that attributes of objects derived from the ensemble members of the five individual EPS forecasts and the observations differ consistently. For instance, their predicted centroid location is more southwestward, their shape is more circular, and their orientation is more meridional than in the observations. The sensitivity of the number of objects and their attributes to methodological parameters is also investigated. An MME prediction technique that is based on weights computed from the object-based scores, median of maximum interest, and object-based threat score is explored and the results are compared with the ensemble forecasts of the individual EPS, the equally weighted MME forecast, and the traditional superensemble forecast. When using MODE statistics for the forecast evaluation, the object-based MME prediction outperforms all other predictions. This is mainly because of a better prediction of the objects’ centroid locations. When using the precipitation-based fractions skill score, which is not used in either of the weighted MME forecasts, the object-based MME forecasts are slightly better than the equally weighted MME forecasts but are inferior to the traditional superensemble forecast that is based on weights derived from the point-to-point metric MAE.