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Reliable projections of tropical cyclone (TC) activities in the western North Pacific (WNP) are crucial for climate policy-making in densely-populated coastal Asia. Existing projections, however, exhibit considerable uncertainties with unclear sources. Here, based on future projections by the latest Coupled Model Intercomparison Project Phase 6 climate models, we identify a new and prevailing source of uncertainty arising from different TC identification schemes. Notable differences in projections of detected TCs and empirical genesis potential indices are found to be caused by inconsistent changes in dynamic and thermodynamic environmental factors affecting TC formations. While model uncertainty holds the secondary importance, we show large potential in reducing it through improved model simulations of present-day TC characteristics. Internal variability noticeably impacts near-term projections of the WNP tropical cyclogenesis, while the relative contribution of scenario uncertainty remains small. Our findings provide valuable insights into model development and TC projections, thereby aiding in adaptation decisions.

Rare precipitation events with return periods of multiple decades to hundreds of years are particularly damaging to natural and societal systems. Projections of such rare, damaging precipitation events in the future climate are, however, subject to large inter‐model variations. We show that a substantial portion of these differences can be ascribed to the projected warming uncertainty, and can be robustly reduced by using the warming observed during recent decades as an observational constraint, implemented either by directly constraining the projections with the observed warming or by conditioning them on constrained warming projections, as verified by extensive model‐based cross‐validation. The temperature constraint reduces >40% of the warming‐induced uncertainty in the projected intensification of future rare daily precipitation events for a climate that is 2°C warmer than preindustrial across most regions. This uncertainty reduction together with validation of the reliability of the projections should permit more confident adaptation planning at regional levels. Plain Language Summary Very rare extreme precipitation events are particularly damaging to natural and societal systems. Projections of such rare, damaging precipitation events in the future climate vary substantially among climate models. Reducing this uncertainty will aid adaptation planning. We show here that the projected range of future rare precipitation intensification is strongly affected by the projected range of global warming, especially for regions where the intensification is dominated by increases in atmospheric moisture. We verify that using the past global warming trend as an observational constraint can eliminate more than 40% of the warming‐induced uncertainty in the intensification of future rare precipitation events at 2°C warmer above preindustrial across most global land regions. This narrowing of the possible range of future rare precipitation intensification at regional scales can greatly benefit adaptation planning. Key Points Projections of future rare precipitation intensification exhibit substantial differences among climate models A substantial portion of the inter‐model differences can be traced to the projected global warming uncertainty Using the past global warming trend as an observational constraint can eliminate >40% of the warming‐induced uncertainty

Accurately projecting climate change and its impact is crucial for quantifying the risk of extreme events and developing effective adaptation strategies. However, future projections exhibit substantial uncertainties among Earth system models (ESMs). Notably, the latest phase of the Coupled Model Intercomparison Project includes some ‘hot’ ESMs with high climate sensitivity that exceed the likely range inferred from multiple lines of evidence, leading to a broader uncertainty range compared to previous CMIP phases. Although various uncertainty quantification and constraint methods have been proposed, they are not yet widely adopted. The approach of using an equal-weighted ensemble average for projections remains prevalent. Here we examine commonly used uncertainty quantification methods and constraint projection methods, describing their characteristics. Subsequently, taking extreme precipitation as a case, we constrain the range of projection uncertainty employing two weighing constraint methods and two emergent constraint methods. The results demonstrate that all methods effectively reduce the uncertainty in extreme precipitation projections. Specifically, the comprehensive constraints reduce the projection uncertainty by 26%–31% at the long-term future (2081–2100) under different scenarios. Therefore, we strongly recommend that attention should be paid to quantifying and constraining uncertainty when undertaking future projections of climate change and its impacts.

A significant warming is projected in Antarctic climate change under high CO2 forcing, involving complex interactions between ocean and land surfaces. While previous studies have emphasized the seasonal mechanism driving Antarctic ocean surface warming, the processes governing land surface warming remain less explored. Here we show that, under abrupt quadrupled CO2 forcing, Antarctic land surface experiences uniform warming throughout the year, primarily driven by poleward atmospheric heat transport, with latent energy transport playing a dominant role. This moisture‐related transport not only delivers energy but also amplifies the water vapor feedback, significantly contributing to the warming. Our findings suggest that the discrepancies in representing these atmospheric processes across models, contribute substantially to the uncertainties in Antarctic land surface warming projections. The result emphasizes the need for improved understanding of the atmospheric dynamics in polar regions to reduce model uncertainties under future climate scenarios.

Predicting the boreal summer climate over East Asia and the western Pacific is crucial for communities preparing for extreme events. A key source of predictability is the strong connection between the western North Pacific anomalous circulation (WNPAC) and the preceding El Niño‐Southern Oscillation (ENSO). However, the potential change of this link under future greenhouse warming remains uncertain due to substantial internal variability and inter‐model discrepancies. Here, by leveraging emergent constraints from multi‐large ensemble simulations, we show that the trans‐seasonal ENSO‐WNPAC correlation robustly strengthens under high‐emission scenarios, with a 67% reduction in the projection uncertainty. This enhancement indicates a 9% increase in the ENSO‐contributed predictability (explained variance) of summer WNPAC. The spread across models primarily derives from their differing representations of ENSO‐decaying regimes. Our results indicate a more predictable East Asian‐western Pacific summer climate in a warmer world, offering encouraging prospects for adapting to anticipated increases in extremes associated with WNPAC.

Although the multi‐model average compares well with observations, individually most of the latest climate models do not simulate a realistic size of the Indo‐Pacific Warm Pool in the present‐day climate. This study explores the implications of this warm pool size bias in climate models in Northern Hemisphere winter. The warm pool size bias in phase 6 of the Coupled Model Intercomparison Project models is related to the subtropical jet and precipitation distribution, both in the present‐day climate and in response to climate change, through extratropical Rossby wave trains and tropical circulation pathways. Based on these relationships, emergent constraints are developed to observationally constrain the future subtropical jet response over Asia and the Atlantic Ocean and precipitation response over North and Central America, which can help to reduce uncertainty in future projections of these features. Thus, accurate model simulation of the warm pool in the present‐day climate is important for future projections of the subtropical jet and precipitation. Plain Language Summary This study examines the impact of a common problem in the latest climate models where they do not accurately simulate the size of the Indo‐Pacific Warm Pool in the present‐day climate. The effects of this issue are investigated in the Northern Hemisphere winter. The warm pool size problem in the models affects the subtropical winds and precipitation, both in the current climate and in response to climate change. Based on the relationships between the present‐day warm pool size and future projections of the subtropical winds and precipitation across models, we can help to constrain future projections of the subtropical winds over Asia and precipitation over North and Central America by ruling out models with biased warm pool size. The results show that having an accurate simulation of the warm pool in the present‐day climate is crucial for more reliable future projections of subtropical winds and precipitation. Key Points Phase 6 of the Coupled Model Intercomparison Project multi‐model‐mean reproduces the observed Indo‐Pacific warm pool size well, but there is a huge inter‐model spread in warm pool size Model biases in warm pool size have local and remote effects on subtropical winds and precipitation in present‐day and future climates Emergent constraints reduce projection uncertainty of subtropical winds and precipitation by ruling out models with biased warm pool size

Climate change adaptation and relevant policy-making need reliable projections of future climate. Methods based on multi-model ensemble are generally considered as the most efficient way to achieve the goal. However, their efficiency varies and inter-comparison is a challenging task, as they use a variety of target variables, geographic regions, time periods, or model pools. Here, we construct and use a consistent framework to evaluate the performance of five ensemble-processing methods, i.e., multi-model ensemble mean (MME), rank-based weighting (RANK), reliability ensemble averaging (REA), climate model weighting by independence and performance (ClimWIP), and Bayesian model averaging (BMA). We investigate the annual mean temperature (Tav) and total precipitation (Prcptot) changes (relative to 1995–2014) over China and its seven subregions at 1.5 and 2 °C warming levels (relative to pre-industrial). All ensemble-processing methods perform better than MME, and achieve generally consistent results in terms of median values. But they show different results in terms of inter-model spread, served as a measure of uncertainty, and signal-to-noise ratio (SNR). ClimWIP is the most optimal method with its good performance in simulating current climate and in providing credible future projections. The uncertainty, measured by the range of 10th-90th percentiles, is reduced by about 30% for Tav, and 15% for Prcptot in China, with a certain variation among subregions. Based on ClimWIP, and averaged over whole China under 1.5/2 °C global warming levels, Tav increases by about 1.1/1.8 °C (relative to 1995–2014), while Prcptot increases by about 5.4%/11.2%, respectively. Reliability of projections is found dependent on investigated regions and indices. The projection for Tav is credible across all regions, as its SNR is generally larger than 2, while the SNR is lower than 1 for Prcptot over most regions under 1.5 °C warming. The largest warming is found in northeastern China, with increase of 1.3 (0.6-1.7)/2.0 (1.4-2.6) °C(ensemble’s median and range of the 10th–90th percentiles) under 1.5/2 °C warming, followed by northern and northwestern China. The smallest but the most robust warming is in southwestern China, with values exceeding 0.9 (0.6–1.1)/1.5 (1.1–1.7) °C. The most robust projection and largest increase is achieved in northwestern China for Prcptot, with increase of 9.1%(-1.6–24.7%)/17.9% (0.5–36.4%) under 1.5/2 °C warming. Followed by northern China, where the increase is 6.0%(-2.6–17.8%)/11.8% (2.4–25.1%), respectively. The precipitation projection is of large uncertainty in southwestern China, even with uncertain sign of variation. For the additional half-degree warming, Tav increases more than 0.5 °C throughout China. Almost all regions witness an increase of Prcptot, with the largest increase in northwestern China.

This study examines the spatiotemporal characteristics of the summer monsoon rainy season over East Asia using six regional climate models (RCMs) participating in the Coordinated Regional Domain Experiment (CORDEX) East Asia Phase II project. The framework combining multiple global climate models (GCMs) with multiple RCMs produces a larger spread in summer monsoon characteristics than driving GCMs only, enabling a better quantification of uncertainty factors. On average, the RCM simulations reproduce the observed summer monsoon duration and area better than the corresponding boundary GCMs, implying the added values of downscaling. Both the area and duration of the East Asian summer monsoon are projected to increase by the late 21st century, more strongly in high emission scenarios than in low emission scenarios, particularly in China. Different responses between scenarios, which indicate warming mitigation benefits, only become significant in the late 21st century due to large intersimulation uncertainties. Analysis of variance results show that uncertainty in future monsoon area and duration is larger between boundary GCMs than between RCMs over East Asia and its coastal subregions. A strong intersimulation relationship between RCMs and GCMs supports that boundary GCMs substantially diversify downscaled RCM projections through different climate sensitivities. Furthermore, the distinct subregional responses in future monsoon area and duration emphasize the importance of fine-resolution projections with appropriate uncertainty measures for better preparing region-specific adaptation plans.

Climate models project a weakening and expansion of the Hadley circulation (HC) under global warming but with considerable spread in the magnitude of these changes. Here, utilizing models from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6), we illustrate how the variance in projected changes in the HC arises from equilibrium climate sensitivity (ECS) uncertainty across models. Models with higher ECS project a greater extent of static stability increase hence larger HC changes. Using the best estimate of ECS with value of 3 K (∼2.5–4.0 K) to constrain the HC projection, we reveal that the constrained projection yields a 15% (11%) decrease in the weakening (poleward shift) of the HC in the Northern (Southern) Hemisphere compared to the multimodel mean under the SSP5-8.5 scenario. The corresponding projection uncertainty is reduced by about 77.4% and 75.6%, respectively. Our results indicate a smaller-than-expected change in the HC in response to increased CO 2 concentrations.

Four bias correction methods, i.e., gamma cumulative distribution function (GamCDF), quantile–quantile adjustment (QQadj), equidistant cumulative probability distribution function (CDF) matching (EDCDF), and transform CDF (CDF-t), to read are applied to five daily precipitation datasets over China produced by LMDZ4-regional that was nested into five global climate models (GCMs), BCC-CSM1-1m, CNRM-CM5, FGOALS-g2, IPSL-CM5A-MR, and MPI-ESM-MR, respectively. A unified mathematical framework can be used to define the four bias correction methods, which helps understanding their natures and essences for identifying the most reliable probability distributions of projected climate. CDF-t is shown to be the best bias correction method based on a comprehensive evaluation of different precipitation indices. Future precipitation projections corresponding to the global warming levels of 1.5 °C and 2 °C under RCP8.5 were obtained using the bias correction methods. The multi-method and multi-model ensemble characteristics allow to explore the spreading of projections, considered a surrogate of climate projection uncertainty, and to attribute such uncertainties to different sources. It was found that the spread among bias correction methods is smaller than that among dynamical downscaling simulations. The four bias correction methods, with CDF-t at the top, all reduce the spread among the downscaled results. Future projection using CDF-t is thus considered having higher credibility.