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The 2021 IPCC report found that most studies show declining trends for the global diurnal temperature range (DTR) since the 1950s, decreasing mainly during 1960–1980. This issue is revisited here using an up‐to‐date in‐situ data set, Hadley Center Integrated Surface Database, constrained by rigorous station selection conditions. The global observed DTR trend was found to reverse during 1980–2021, increasing significantly at a rate of 0.091 ± 0.008°C decade−1. The trend was dominated by a faster rate of increasing daily maximum air temperature. This increasing observed trend in the past four decades was not fully captured in raw CMIP6 models, as models only partially capture the spatial patterns. With global CMIP6 outputs and regionally‐available observations, the global land DTR was then estimated, through emergent constraints, to be 0.063 ± 0.012°C decade−1. The study raises concern for risks of increasing DTR globally and provides new insights into global DTR assessment. Plain Language Summary In 2021, the IPCC reported a decrease in the near‐surface diurnal air temperature range (DTR) since the 1950s. However, using the in‐situ surface air temperature observations, the global DTR trend was found to reverse after the 1980s, as daily maximum air temperature increased faster than the daily minimum air temperature did between 1980 and 2021. The observed results for 1980–2014 were used to assess the historical simulations within CMIP6. Models generally depicted similar spatial variability as observed results but high variation existed between models. Most of the models did not capture the reversal of the global DTR trend and underestimated regional results. To narrow down the uncertainty and produce a complete global land DTR estimation, we applied the emergent constraint approach by combining observation data and model results. The global DTR trend from 1980 to 2014 was 0.063 ± 0.012°C decade−1. The constraining data was also used at a regional scale. It was shown that DTR trends for North America retained high uncertainty (−0.011 ± 0.026°C decade−1), while Europe and Asia showed reduced uncertainty with increasing DTR. Key Points The up‐to‐date Hadley Center Integrated Surface Database (HadISD) in‐situ data reveals a reversed global diurnal air temperature range (DTR) trend, increasing for 1980–2021 The observed spatial patterns are partially captured but the reversal trends are not shown by CMIP6 models The emergent constraint for CMIP6 models with HadISD estimates increasing DTR at the global scale

Reliable projections of the tropical Pacific sea surface temperature (SST) warming (TPSW) patterns are critically important for exploring the future climate change. However, climate models suffer from long‐standing common biases in simulating the present‐day climate, raising doubts about the model projected TPSW patterns. Here by using outputs from 30 CMIP6 models, we find the projected TPSW patterns are significantly correlated with the simulated present‐day SST in the tropical North Atlantic (TNA), with higher present‐day TNA SSTs tending to project more weakened zonal SST gradients by producing more present‐day low‐level clouds and the resultant positive cloud–shortwave–SST feedbacks over the eastern equatorial Pacific. An emergent constraint using observed TNA SST reveals a consistent El Niño‐like warming pattern in all models with more weakened zonal SST gradient than before in most models, together with a reduction of the inter‐model uncertainty in the zonal SST gradient change by more than 20%. Plain Language Summary Future projections of the tropical Pacific SST warming (TPSW) pattern remain highly uncertain. One of the key reasons is that climate models suffer from several long‐standing common biases in simulating the current climate state. Here we find that the remote common cold SST bias in the tropical North Atlantic acts to suppress the future SST warming over the eastern equatorial Pacific through a trans‐basin atmospheric connection. By removing the effect of the TNA cold SST bias from model projections, the corrected TPSW displays a more El Niño‐like SST warming pattern with more weakened zonal SST gradient, together with a reduction of the inter‐model uncertainty by more than 20%. Key Points Models with a higher (lower) present‐day tropical North Atlantic (TNA) sea surface temperature (SST) tend to project a more (less) El Niño‐like SST warming pattern The TNA cold SST bias leads to a lack of low‐level cloud over the eastern Pacific by weakening the regional Hadley‐type circulation Spatial constraints on the projected tropical Pacific SST warming from the observed TNA SST suggest a more El Niño‐like warming pattern

A reliable projection of the future duration of the Indian summer monsoon (ISM) provides an important input for climate adaptation in the Indian subcontinent. Nevertheless, there is low confidence for projections of ISM duration, due to large inter‐model uncertainty of onset and withdrawal changes. Here, we find that models with excessive sea surface temperature (SST) over the tropical western Pacific (WP) during spring and greater surface warming trends over the northern mid‐high latitudes (NMHL) during autumn in the present day tend to overestimate future delays to ISM onset and withdrawal, respectively. This can be attributed to the influence of surface thermal conditions on upper‐tropospheric warming patterns. Constrained by the observational WP SST and NMHL surface warming trends, projected ISM duration under a high‐emission scenario is shortened by 6 days compared to the current climate, with a reduction of inter‐model uncertainty by 46% relative to the unconstrained results. Plain Language Summary The duration of the Indian summer monsoon (ISM) has profound implications for socioeconomic activity in the Indian subcontinent, given its strong link with the length of India's rainy season, which directly impacts water resources, crops and fisheries. The potential future change of ISM duration remains inconclusive due to large inter‐model discrepancies between models. Here, we find that the model uncertainties of climatic change in the onset and withdrawal of the ISM are closely related to the modeled present‐day sea surface temperatures in the tropical western Pacific Ocean and the surface warming trend over Northern Hemisphere mid‐high latitudes, respectively. By constraining present‐day simulations of the two metrics using observations, the projected ISM duration becomes shorter, and the uncertainty is reduced significantly in comparison to unconstrained estimates. The results imply that more and more intense extreme rainfall events will occur within a shorter season, which will significantly increase the impact of the hydrological disasters associated with extreme rainfall events such as flash floods and landslides. Key Points Spring sea surface temperature over the tropical West Pacific is used as an observational constraint for future monsoon onset projections Autumn surface warming trends over northern mid‐high latitudes can constrain future changes in monsoon withdrawal Constrained projections show a more reliable reduction in Indian summer monsoon duration compared to unconstrained projections

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.

Snow cover is a critical component of climate, hydrological, and ecological systems, particularly in high‐altitude and high‐latitude regions. Global warming has driven substantial snow cover retreat, yet projections from climate models vary widely, hindering reliable climate change adaptation and policy planning. For the Tibetan Plateau (TP), observationally constrained projections offer an opportunity to improve the credibility of snow cover projections, yet such applications are still limited. Based on climate models, we find that the historical inter‐model snow cover reductions are strongly correlated with the projected remaining snow cover by the end of the 21st century. By this emergent relationship and satellite‐derived products, we calibrate the best estimate of the remaining frequency of spring snow cover (relative to 1981–2020) to 37%, which is lower than the multi‐models' prediction (55%) and substantially reduces inter‐model spread (17% after constraint compared to 23% in standard deviation) in a high emission scenario. Cross‐validation tests reinforce the robustness of the constrained projection. The underestimation of snow cover loss is likely attributed to an underestimation of observed warming magnitude. Sensitivity analyses indicate that the choice of period for historical snow cover extent reduction trends and climate model ensemble are the main sources of uncertainty, whereas differences between the two satellite‐derived data sets have only a limited impact on the constraint.

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.

An Emergent Constraint (EC) is a physically-explainable relationship between model simulations of a past climate variable (predictor) and projections of a future climate variable (predictand). If a significant correlation exists between the predictand and the predictor, observations of the latter can be used to constrain model projections of the former and to narrow their uncertainties. In the present study, the EC technique has been applied to the analysis of precipitation, one of the variables most affected by model uncertainties and still insufficiently analysed in the context of ECs, particularly for the recent CMIP6 model ensemble. The main challenge in determining an EC is establishing if the relationship found is physically meaningful and robust to the composition of the model ensemble. Four precipitation ECs already documented in the literature and so far tested only with CMIP3/CMIP5, three of them involving the analysis of extreme precipitation, have been reconsidered in this paper. Their existence and robustness are evaluated using different subsets of CMIP5 and CMIP6 models, verifying if the EC is still present in the most recent ensemble and assessing its sensitivity to the detailed ensemble composition. Most ECs considered do not pass this test: we found one EC not to be robust in both CMIP5 and CMIP6, other two exist and are robust in CMIP5 but not in CMIP6, and only one is verified and is robust in both model ensembles.

The summer monsoon onset over the Bay of Bengal and South China Sea signals the beginning of the Asian summer monsoon, critical for local fisheries, agriculture and livelihoods, so communities are concerned about its potential changes under global warming. Previous projections have suggested a delay, but the extent of this delay remains uncertain, undermining the reliability of the projections. Here, we show a significant correlation between the projected shift in Bay of Bengal/South China Sea monsoon onset and present‐day sea surface temperature (SST) simulation over the western Pacific (WP). This emergent relationship arises from the spread of the precipitation response over the western‐central Pacific to WP SST, as more precipitation induces stronger tropical upper‐tropospheric warming, increasing westerly vertical shear near South Asia, and facilitating the onset delay. The rectified projections indicate that the delayed shift is almost halved compared to raw projections, and the intermodel uncertainty is reduced by 30%. Plain Language Summary The summer monsoon onset over the Bay of Bengal and South China Sea is a crucial time for fishing, agriculture and livelihood, and is important for the further advance of the monsoon rains over tropical Asia. Previous studies have suggested that monsoon onset in these seas will be delayed in the future, but there is large uncertainty between models, undermining the reliability of the projection. This study finds that the projected shift in the onset is strongly related to a model's representation of sea surface temperatures over the western Pacific Ocean in the present day. Based on the relationship found in observations, we adjust or “constrain” the future projections of the monsoon onset. The delay to the monsoon onset using this adjustment is almost half of the original projection, and the uncertainty between models is also reduced. We suggest that a more accurate simulation of sea‐surface temperatures in the present‐day climate of the western Pacific Ocean is crucial for reliable future projections of the monsoon onset. Key Points An observational constraint is identified for projected changes in summer monsoon onset date over the Bay of Bengal and South China Sea Models with a warmer sea surface temperature bias over the tropical western Pacific tend to project a more delayed monsoon onset in the future The emergent constraint indicates that the delay of monsoon onset over the Bay of Bengal and South China Sea is overestimated

The Tibetan Plateau (TP) is an area highly sensitive to climate change and is warming faster than the global average. The TP temperature change has a significant impact on the local ecological environment and the downstream weather and climate. The TP will undoubtedly warm in the future, but the warming extent is uncertain. Using the Coupled Model Inter‐comparison Project Phase 6 multi‐model ensemble, we found that models simulating smaller TP temperature increases in recent decades tend to project weaker warming in the future. This relationship is driven by the simulation of snowmelt response to greenhouse gas increases, as snow‐related albedo feedback dominates the TP temperature changes in both historical and future periods. Based on a two‐step emergent constraint approach, the rectified TP warming magnitude increases by about 0.3°C compared to the unconstrained result under both the medium and high emission scenarios, and the inter‐model uncertainty is reduced by about 60%. Plain Language Summary The Tibetan Plateau (TP), known as “the third pole of the world,” is the highest terrain on Earth. It has experienced a rapid increase in surface temperature over the past few decades, which has significantly impacted the local ecological environment and downstream weather and climate. Therefore, how the surface temperature of the TP will change in the future is of concern to both the scientific community and society at large. Although a future warming trend over the TP is evident in model projections, the extent of this warming remains highly uncertain. Based on a multi‐model ensemble, we found that the projected temperature change over the TP during the boreal winter is significantly influenced by the simulation of TP temperature changes in recent decades, which is related to model sensitivities in simulating the snowmelt rate in response to greenhouse gas increases. Removing the model bias using observations would further increase the projected future warming over the TP, meaning that humanity will face greater challenges. Key Points A two‐step emergent constraint (EC) technique is developed for projections of the Tibetan Plateau (TP) warming in the boreal winter The EC arises from model sensitivities in simulating snow cover change in response to greenhouse gas increases Constrained results effectively reduce inter‐model uncertainty and show stronger warming over the TP than the original results

Global climate model (GCM) projections of future climate are uncertain largely due to a persistent spread in cloud feedback. This is despite efforts to reduce this model uncertainty through a variety of emergent constraints (ECs); with several studies suggesting an important role for present‐day biases in clouds. Here, we use three generations of GCMs to assess the value of climatological cloud metrics for constraining uncertainty in cloud feedback. We find that shortwave cloud radiative properties across the Southern Hemisphere extratropics are most robustly correlated with tropical cloud feedback (TCF). Using this relationship in conjunction with observations, we produce an EC that yields a TCF value of 0.52 ± 0.34 W/m2/K, which equates to a 34% reduction in uncertainty. Thus, we show that climatological cloud properties can be used to reduce uncertainty in how clouds will respond to future warming. Plain Language Summary Different global climate models exhibit large variability in how clouds across the tropics will respond to future warming. This is largely due to the complexity and diversity of responses that differing cloud types may experience under warming. A long‐term goal of the community has been to narrow this disagreement between different models. Over the past 15 years, several studies have proposed ways in which the variability in future cloud changes might be related to errors in how these models represent present‐day properties. Here, we use three collections of models to show that variability in tropical cloud changes is closely tied to shortwave cloud radiative properties across the Southern Ocean. We then use this intermodel relationship along with observations to produce a best estimate of cloud feedback across the tropics. Key Points We find a relationship between tropical cloud feedback and mean‐state biases in Southern Hemisphere extratropical cloud properties This intermodel relationship is found to be present in three different ensembles of global climate models, a sign of robustness This relationship suggests a likely tropical cloud feedback value of 0.52 ± 0.34 W/m2/K, which equates to a 34% reduction in uncertainty