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Conditional instability and the buoyancy of plumes drive moist convection but have a variety of representations in model convective schemes. Vertical thermodynamic structure information from Atmospheric Radiation Measurement (ARM) sites and reanalysis (ERA5), satellite-derived precipitation (TRMM3b42), and diagnostics relevant for plume buoyancy are used to assess climate models. Previous work has shown that CMIP6 models represent moist convective processes more accurately than their CMIP5 counterparts. However, certain biases in convective onset remain pervasive among generations of CMIP modeling efforts. We diagnose these biases in a cohort of nine CMIP6 models with subdaily output, assessing conditional instability in profiles of equivalent potential temperature,θ_e, and saturation equivalent potential temperature,θ_es, in comparison to a plume model with different mixing assumptions. Most models capture qualitative aspects of theθ_esvertical structure, including a substantial decrease with height in the lower free troposphere associated with the entrainment of subsaturated air. We define a “pseudo-entrainment” diagnostic that combines subsaturation and aθ_esmeasure of conditional instability similar to what entrainment would produce under the small-buoyancy approximation. This captures the trade-off between largerθ_eslapse rates (entrainment of dry air) and small subsaturation (permits positive buoyancy despite high entrainment). This pseudo-entrainment diagnostic is also a reasonable indicator of the critical value of integrated buoyancy for precipitation onset. Models with poorθ_e/θ_esstructure (those using variants of the Tiedtke scheme) or low entrainment runs of CAM5, and models with low subsaturation, such as NASA-GISS, lie outside the observational range in this diagnostic.

Accurate precipitation monitoring is crucial for understanding climate change and rainfall-driven hazards at a local scale. However, the current suite of monitoring approaches, including weather radar and rain gauges, have different insufficiencies such as low spatial and temporal resolution and difficulty in accurately detecting potentially destructive precipitation events such as hailstorms. In this study, we develop an array-based method to monitor rainfall with seismic nodal stations, offering both high spatial and temporal resolution. We analyze seismic records from 1825 densely spaced, high-frequency seismometers in Oklahoma, and identify signals from nine precipitation events that occurred during the one-month station deployment in 2016. After removing anthropogenic noise and Earth structure response, the obtained precipitation spatial pattern mimics the one from a nearby operational weather radar, while offering higher spatial (~ 300 m) and temporal (< 10 s) resolution. We further show the potential of this approach to monitor hail with joint analysis of seismic intensity and independent precipitation rate measurements, and advocate for coordinated seismological-meteorological field campaign design.

Orographically‐locked diurnal convection involves interactions between local circulation and the thermodynamic environment of convection. Here, the relationships of convective updraft structures over orographic precipitation hotspots and their upstream environment in the TaiwanVVM large‐eddy simulations are analyzed for the occurrence of the orographic locking features. Strong convective updraft columns within heavily precipitating, organized systems exhibit a mass flux profile gradually increasing with height through a deep lower‐tropospheric inflow layer. Enhanced convective development is associated with higher upstream moist static energy (MSE) transport through this deep‐inflow layer via local circulation, augmenting the rain rate by 36% in precipitation hotspots. The simulations provide practical guidance for targeted observations within the most common deep‐inflow path. Preliminary field measurements support the presence of high MSE transport within the deep‐inflow layer when organized convection occurs at the hotspot. Orographically‐locked convection facilitate both modeling and field campaign design to examine the general properties of active deep convection.

The prediction skill for precipitation anomalies in late spring and summer months—a significant component of extreme climate events—has remained stubbornly low for years. This paper presents a new idea that utilizes information on boreal spring land surface temperature/subsurface temperature (LST/SUBT) anomalies over the Tibetan Plateau (TP) to improve prediction of subsequent summer droughts/floods over several regions over the world, East Asia and North America in particular. The work was performed in the framework of the GEWEX/LS4P Phase I (LS4P-I) experiment, which focused on whether the TP LST/SUBT provides an additional source for subseasonal-to-seasonal (S2S) predictability. The summer 2003, when there were severe drought/flood over the southern/northern part of the Yangtze River basin, respectively, has been selected as the focus case. With the newly developed LST/SUBT initialization method, the observed surface temperature anomaly over the TP has been partially produced by the LS4P-I model ensemble mean, and 8 hotspot regions in the world were identified where June precipitation is significantly associated with anomalies of May TP land temperature. Consideration of the TP LST/SUBT effect has produced about 25–50% of observed precipitation anomalies in most hotspot regions. The multiple models have shown more consistency in the hotspot regions along the Tibetan Plateau-Rocky Mountain Circumglobal (TRC) wave train. The mechanisms for the LST/SUBT effect on the 2003 drought over the southern part of the Yangtze River Basin are discussed. For comparison, the global SST effect has also been tested and 6 regions with significant SST effects were identified in the 2003 case, explaining about 25–50% of precipitation anomalies over most of these regions. This study suggests that the TP LST/SUBT effect is a first-order source of S2S precipitation predictability, and hence it is comparable to that of the SST effect. With the completion of the LS4P-I, the LS4P-II has been launched and the LS4P-II protocol is briefly presented.

Observations and cloud‐resolving simulations suggest that a convective updraft structure drawing mass from a deep lower‐tropospheric layer occurs over a wide range of conditions. This occurs for both mesoscale convective systems (MCSs) and less‐organized convection, raising the question: is there a simple, universal characteristic governing the deep inflow? Here, we argue that nonlocal dynamics of the response to buoyancy are key. For precipitating deep‐convective features including horizontal scales comparable to a substantial fraction of the troposphere depth, the response to buoyancy tends to yield deep inflow into the updraft mass flux. Precipitation features in this range of scales are found to dominate contributions to observed convective precipitation for both MCS and less‐organized convection. The importance of such nonlocal dynamics implies thinking beyond parcel models with small‐scale turbulence for representation of convection in climate models. Solutions here lend support to investment in parameterizations at a complexity between conventional and superparameterization.

This study examines thermodynamic–convection coupling in observations and reanalyses, and attempts to establish process-level benchmarks needed to guide model development. Thermodynamic profiles obtained from the NOAA Integrated Global Radiosonde Archive, COSMIC-1 GPS radio occultations, and several reanalyses are examined alongside Tropical Rainfall Measuring Mission precipitation estimates. Cyclical increases and decreases in a bulk measure of lower-tropospheric convective instability are shown to be coupled to the cyclical amplification and decay of convection. This cyclical flow emerges from conditional-mean analysis in a thermodynamic space composed of two components: a measure of “undiluted” instability, which neglects lower-free-tropospheric (LFT) entrainment, and a measure of the reduction of instability by LFT entrainment. The observational and reanalysis products examined share the following qualitatively robust characterization of these convective cycles: increases in undiluted instability tend to occur when the LFT is less saturated, are followed by increases in LFT saturation and precipitation rate, which are then followed by decreases in undiluted instability. Shallow, convective, and stratiform precipitation are coupled to these cycles in a manner consistent with meteorological expectations. In situ and satellite observations differ systematically from reanalyses in their depictions of lower-tropospheric temperature and moisture variations throughout these convective cycles. When using reanalysis thermodynamic fields, these systematic differences cause variations in lower-free-tropospheric saturation deficit to appear less influential in determining the strength of convection than is suggested by observations. Disagreements among reanalyses, as well as between reanalyses and observations, pose significant challenges to process-level assessments of thermodynamic–convection coupling.

Precipitation clusters are spatially contiguous precipitating regions. Large clusters in the tropics are rare, extreme events that include organized precipitating systems. Changes to the probability distributions of tropical precipitation clusters under global warming are examined using models from the coupled model intercomparison project Phase 6 (CMIP6). Every analyzed model projects significant increases in frequencies of both very large‐sized clusters and clusters with very large area‐integrated precipitation (cluster power). The occurrence probability for the highest historical cluster power values increases by a factor between 4 and 15 among models in the end‐of‐century SSP5‐8.5 scenario. These changes primarily occur over the precipitating tropics: the western Pacific, Indian subcontinent, central and east Pacific convergence zones, and parts of South America. This spatial pattern is largely explained by Clausius‐Clapeyron scaling of current climate cluster power values. Societal impacts of cluster power increases could be acute in coastal regions of the Indian subcontinent and western Pacific islands.

Precipitation sustains life and supports human activities, making its prediction one of the most societally relevant challenges in weather and climate modeling. Limitations in modeling precipitation underscore the need for diagnostics and metrics to evaluate precipitation in simulations and predictions. While routine use of basic metrics is important for documenting model skill, more sophisticated diagnostics and metrics aimed at connecting model biases to their sources and revealing precipitation characteristics relevant to how model precipitation is used are critical for improving models and their uses. This paper illustrates examples of exploratory diagnostics and metrics including 1) spatiotemporal characteristics metrics such as diurnal variability, probability of extremes, duration of dry spells, spectral characteristics, and spatiotemporal coherence of precipitation; 2) process-oriented metrics based on the rainfall–moisture coupling and temperature–water vapor environments of precipitation; and 3) phenomena-based metrics focusing on precipitation associated with weather phenomena including low pressure systems, mesoscale convective systems, frontal systems, and atmospheric rivers. Together, these diagnostics and metrics delineate the multifaceted and multiscale nature of precipitation, its relations with the environments, and its generation mechanisms. The metrics are applied to historical simulations from phases 5 and 6 of the Coupled Model Intercomparison Project. Models exhibit diverse skill as measured by the suite of metrics, with very few models consistently ranked as top or bottom performers compared to other models in multiple metrics. Analysis of model skill across metrics and models suggests possible relationships among subsets of metrics, motivating the need for more systematic analysis to understand model biases for informing model development.

A process‐oriented diagnostic (POD) is introduced to measure the thermodynamic sensitivity of convection in climate models. The physical basis for this POD is the observed tropical precipitation‐buoyancy relationship. Fast timescale precipitation and thermodynamic profiles over oceans are POD inputs; these are used to evaluate model precipitation sensitivities to lower‐tropospheric measures of subsaturation (SUBSAT_L) and undilute conditional instability. The POD is used to diagnose 24 coupled model inter‐comparison project phase six (CMIP6) models. Half the diagnosed models exhibit SUBSAT_Lsensitivity close to observed, while six models are excessively sensitive. Parameter perturbation experiments with the Community Atmospheric Model (CAM5) support the physical basis for the POD. Increasing entrainment increases the CAM5 precipitation SUBSAT_Lsensitivity. Switching off the convective scheme or modifying the convective trigger to be oversensitive to moisture reproduces the excessive SUBSAT_Lsensitivity seen among CMIP6 models. Models with excessive SUBSAT_Lsensitivities have precipitating mean states closer to grid‐scale saturation and likely support more grid‐scale convection.

Quantitative simulation of precipitation in current climate has been an ongoing challenge for global climate models. Despite serious biases in correctly simulating probabilities of extreme rainfall events, model simulations under global warming scenarios are routinely used to provide estimates of future changes in these probabilities. To minimize the impact of model biases, past literature tends to evaluate fractional (instead of absolute) changes in probabilities of precipitation extremes under the assumption that fractional changes would be more reliable. However, formal tests for the validity of this assumption have been lacking. Here we evaluate two measures that address properties important to the correct simulation of future fractional probability changes of precipitation extremes, and that can be assessed with current climate data. The first measure tests climate model performance in simulating the characteristic shape of the probability of occurrence of daily precipitation extremes and the second measure tests whether the key parameter governing the scaling of this shape is well reproduced across regions and seasons in current climate. Contrary to concerns regarding the reliability of global models for extreme precipitation assessment, our results show most models lying within the current range of observational uncertainty in these measures. Thus, most models in the Coupled Model Intercomparison Project Phase 6 ensemble pass two key tests in current climate that support the usefulness of fractional measures to evaluate future changes in the probability of precipitation extremes.