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This study assesses the suitability of convolutional neural networks (CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September (JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa, particularly in providing improved forecast products which are essential for end users.

East Africa is particularly vulnerable to precipitation variability, as the livelihood of much of the population depends on rainfed agriculture. Seasonal forecasts of the precipitation anomalies, when skillful, can therefore improve implementation of coping mechanisms with respect to food security and water management. This study assesses the performance of Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0) on forecasting June–September (JJAS) seasonal precipitation anomalies over East Africa. The skill in predicting the JJAS mean precipitation initiated from 1 May for the period of 1982–2019 is evaluated using both deterministic and probabilistic verification metrics on grid cell and over six distinct clusters. The results show that NUIST-CFS1.0 captures the spatial pattern of observed seasonal precipitation climatology, albeit with dry and wet biases in a few parts of the region. The model has positive skill across a majority of Ethiopia, Kenya, Uganda, and Tanzania, whereas it doesn’t exceed the skill of climatological forecasts in parts of Sudan and southeastern Ethiopia. Positive forecast skill is found over regions where the model shows better performance in reproducing teleconnections related to oceanic SST. The prediction performance of NUIST-CFS1.0 is found to be on a level that is potentially useful over a majority of East Africa.

To address the effect of stratiform latent heating on meso‐ to large‐scale circulations, an enhanced implementation of the Multiscale Coherent Structure Parameterization (MCSP) is developed for the Met Office Unified Model. MCSP represents the top‐heavy stratiform latent heating from under‐resolved organized convection in general circulation models. We couple the MCSP with a mass‐flux convection scheme (CoMorph‐A) to improve storm lifecycle continuity. The improved MCSP trigger is specifically designed for mixed‐phase deep convective cloud, combined with a background vertical wind shear, both known to be crucial for stratiform development. We also test a cloud top temperature dependent convective‐stratiform heating partitioning, in contrast to the earlier fixed partitioning. Assessments from ensemble weather forecasts and decadal simulations demonstrate that MCSP directly reduces cloud deepening and precipitation areas by moderating mesoscale circulations. Indirectly, it amends tropical precipitation biases, notably correcting dry and wet biases over India and the Indian Ocean, respectively. Remarkably, the scheme outperforms a climate model ensemble by improving seasonal precipitation cycle predictions in these regions. The scheme also improves Madden‐Julian Oscillation (MJO) spectra, achieving better alignment with observational and reanalysis data by intensifying the simulated MJO over the Indian Ocean during phases 4 to 5. However, the scheme increases precipitation overestimation over the Western Pacific. Shifting from fixed to temperature‐dependent convective‐stratiform partitioning reduces the Pacific precipitation overestimation and further improves the seasonal cycle in India. Spatially correlated biases highlight the necessity for advances beyond deterministic approaches to align MCSP with environmental conditions. Plain Language Summary We improve a key component of the Met Office's climate model to better represent how widespread light rain in severe convective storms affects atmospheric patterns. We make the representation respond to different temperatures at the tops of clouds, which is a new approach compared to previous studies where the heavy‐to‐light rain partitioning was assumed constant. We find that the improved representation of the light rain associated with severe storms reduces the overall storm rainfall area and suppresses storm height. The changes are particularly effective in correcting rainfall forecasts in tropical regions like India and the Indian Ocean, outperforming other climate models. Notably, our model excels at predicting specific tropical climate patterns, enhancing wind and rainfall accuracy over the Maritime Continent. However, our model predicts too much rain over the Western Pacific. When we adjust how we calculate the heavy‐to‐light rain partitioning based on cloud top temperatures, it reduces this overestimation but also weakens our improvements elsewhere. This indicates that we need to keep exploring new approaches to consider the light rain probability in severe storms. Key Points Organized convection scheme with realistic triggering and state‐dependent partitioning between stratiform and convective heating is proposed Scheme leads to reduced storm area and shallower parameterized convection through lower‐troposphere cooling from stratiform precipitation Scheme corrects systematic precipitation biases over India and the Indian Ocean, and also intensifies the Madden‐Julian Oscillation

We characterize trends in maximum seasonal daily precipitation (seasonal Rx1day), minimum (Tn), and maximum (Tx) daily temperatures during days with precipitation over continental Chile for the period 1979–2017, using surface stations and the AgERA5 gridded product derived from the ERA5 reanalysis dataset. We also examine seasonal trends of Sea Surface Temperature (SST), Precipitable Water (PW), Convective Available Potential Energy (CAPE), Eddy Kinetic Energy (EKE), Atmospheric Rivers (ARs) frequency, and upper air observations to seek possible mechanisms that explain precipitation trends. Our results show an increase in seasonal Rx1day during fall in the south part of Northern Chile (15–30°S) and during fall and winter in Austral Chile (45–57°S), and mostly negative trends in Central Chile (30–36°S), where a few locations with positive trends along the coast during summer. Temperature trends presented cooling patterns north of 33°S in almost all the seasons (< -2 °C/dec), while warming trends prevail south of 38°S (> 1 °C/dec). The highest values in Tn trends are obtained on the western slopes of the Andes around 30°S. We also explore temperature scaling in surface stations, finding strong positive super Clausius Clapeyron with Tn, especially between fall and spring in the 33–40°S region. Sounding observations in five stations across Chile suggest warming trends at 23.5°, 33°S, and 53°S, with a stabilization effect by enhanced warming in the upper troposphere, while presenting cooling trends in Puerto Montt (41.5°S). Seasonal trends in PW reveal moistening along southern Peru and northern Chile during spring and summer. Positive trends in CAPE are observed over 35–40°S (austral summer and fall) and the north Altiplano (autumn). SST analyses reveal strong cooling around 30°S in winter, which may explain the negative trends in seasonal Rx1day in central Chile. A warming spot on the northern Peruvian coast during fall may be responsible for humidification in front of Northern Chile, particularly during summer and fall. Positive EKE trends are detected south of 40°S, being stronger and reaching almost all of the coast during spring. ARs frequency unveils negative trends up to -5 days/dec during summer and positive trends of 1 day/dec in 40°- 50°S coastal regions during spring. More generally, the results presented here shed light on the main large-scale processes driving recent trends in precipitation extremes across continental Chile.

Skillful sub-seasonal precipitation forecasts can provide valuable information for both flood and drought disaster mitigations. This study evaluates both deterministic and probabilistic sub-seasonal precipitation forecasts of ECMWF, ECCC, and UKMO models derived from the Sub-seasonal to Seasonal (S2S) Database at various spatiotemporal scales over China during the boreal summer monsoon. The Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), is used as the reference dataset to evaluate the forecast skills of the models. The results suggest that skillful deterministic sub-seasonal precipitation forecasts are found when the lead time is within 2 weeks. The deterministic forecast skills reduce quickly when the lead time is beyond 2 weeks. Positive ranked probability skill scores (RPSS) are only found when the lead time is within 2 weeks for probabilistic forecasts as well. Multimodel ensembling helps to improve forecast skills by removing large negative skill scores in northwestern China. The forecast skills are also improved at larger spatial scales or longer temporal scales. However, the improvement is only observed for certain regions where the predictable low frequency signals remain at longer lead times. The composite analysis suggests that both the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO) have an impact on weekly precipitation variability over China. The forecast skills are found to be enhanced during active ENSO and MJO phases. In particular, the forecast skills are found to be enhanced during active MJO phases.

Impact of future climate change on watershed-scale precipitation was investigated over Northern California based on future climate projections by means of the dynamical downscaling approach. Thirteen different future climate projection realizations from two general circulation models (GCMs: ECHAM5 and CCSM3) based on four emission scenarios (SRES A1B, A1FI, A2, and B1) were dynamically downscaled to 9-km resolution grids over eight watersheds in Northern California for a period of 90 water years (2010–2100). Analysis of daily precipitation over the eight watersheds showed that precipitation values obtained from dynamical downscaling of the 1981 to 1999 control runs of ECHAM5 and CCSM3 GCMs compared well with the PRISM data. Long-term future trends of annual and seasonal basin-average precipitation were investigated. Although a large variability exists for the projected annual basin-average precipitation within each of the 13 individual realizations, there was no significant long-term trend over the eight study watersheds except for the downward trend in the A1FI scenario. On the other hand, significant upward and downward trends were detected in the seasonal basin-average precipitation except in the winter months (January, February, and March). The trend analysis results in this study indicated the importance of considering seasonal variability, scenario, and model uncertainty.

Soil enthalpy （H） contains the combined effects of both soil moisture （w） and soil temperature （T） in the land surface hydrothermal process. In this study, the sensitivities of H to w and T are investigated using the multi-linear regression method. Results indicate that T generally makes positive contributions to H, while w exhibits different （positive or negative） impacts due to soil ice effects. For example, w negatively contributes to H if soil contains more ice; however, after soil ice melts, w exerts positive contributions. In particular, due to lower w interannual variabilities in the deep soil layer （i.e., the fifth layer）, H is more sensitive to T than to w. Moreover, to compare the potential capabilities of H, w and T in precipitation （P） prediction, the Huanghe-Huaihe Basin （HHB） and Southeast China （SEC）, with similar sensitivities of H to w and T, are selected. Analyses show that, despite similar spatial distributions of H-P and T-P correlation coefficients, the former values are always higher than the latter ones. Furthermore, H provides the most effective signals for P prediction over HHB and SEC, i.e., a significant leading correlation between May H and early summer （June） P. In summary, H, which integrates the effects of T and w as an independent variable, has greater capabilities in monitoring land surface heating and improving seasonal P prediction relative to individual land surface factors （e.g., T and w）.

California experienced a historic run of nine consecutive landfalling atmospheric rivers (ARs) in three weeks’ time during winter 2022/23. Following three years of drought from 2020 to 2022, intense landfalling ARs across California in December 2022–January 2023 were responsible for bringing reservoirs back to historical averages and producing damaging floods and debris flows. In recent years, the Center for Western Weather and Water Extremes and collaborating institutions have developed and routinely provided to end users peer-reviewed experimental seasonal (1–6 month lead time) and subseasonal (2–6 week lead time) prediction tools for western U.S. ARs, circulation regimes, and precipitation. Here, we evaluate the performance of experimental seasonal precipitation forecasts for winter 2022/23, along with experimental subseasonal AR activity and circulation forecasts during the December 2022 regime shift from dry conditions to persistent troughing and record AR-driven wetness over the western United States. Experimental seasonal precipitation forecasts were too dry across Southern California (likely due to their overreliance on La Niña), and the observed above-normal precipitation across Northern and Central California was underpredicted. However, experimental subseasonal forecasts skillfully captured the regime shift from dry to wet conditions in late December 2022 at 2–3 week lead time. During this time, an active MJO shift from phases 4 and 5 to 6 and 7 occurred, which historically tilts the odds toward increased AR activity over California. New experimental seasonal and subseasonal synthesis forecast products, designed to aggregate information across institutions and methods, are introduced in the context of this historic winter to provide situational awareness guidance to western U.S. water managers.

Improvements in skill of summer forecasted precipitation as produced by the North American multi‐model ensemble (NMME) system over the contiguous United States (CONUS) are examined by applying a new bias correction method. The uncorrected precipitation produced by NMME hindcasts exhibits good prediction skill in fall and winter, while the spring and summer forecasts are marked with extremely poor skill. We propose a new method to correct the forecasted precipitation distribution based on skillfully predicted 2‐m air temperature (T2m) forecasts to fully exploit the stronger co‐variability that exists between precipitation and T2m in nature. The occurrence of enhanced recycled precipitation over CONUS provides an ideal situation to hone precipitation forecast skills using the T2m forecasts. The proposed bias correction is shown to successfully reduce the root mean square error in precipitation hindcasts in summer and can easily be extended to real‐time forecasts, thus providing a framework to dynamically link precipitation with other predictors besides T2m. Process understanding of the observed T2m‐precipitation relation will offer a framework for diagnosing poor model skill. Improvements in skill of summer forecasted precipitation as produced by the North American multi‐model ensemble (NMME) system over the contiguous United States (CONUS) are examined by applying a new bias correction method. The uncorrected precipitation produced by NMME hindcasts exhibits good prediction skill in fall and winter, while the spring and summer forecasts are marked with extremely poor skill. We propose a new method to correct the forecasted precipitation distribution based on skillfully predicted 2‐m air temperature (T2m) forecasts to fully exploit the stronger co‐variability that exists between precipitation and T2m in nature. The occurrence of enhanced recycled precipitation over CONUS provides an ideal situation to hone precipitation forecast skills using the T2m forecasts. The proposed bias correction is shown to successfully reduce the root mean square error in precipitation hindcasts in summer and can easily be extended to real‐time forecasts, thus providing a framework to dynamically link precipitation with other predictors besides T2m. Process understanding of the observed T2m‐precipitation relation will offer a framework for diagnosing poor model skill.

The climate modelling community has trialled a large number of metrics for evaluating the temporal performance of general circulation models (GCMs), while very little attention has been given to the assessment of their spatial performance, which is equally important. This study evaluated the performance of 36 Coupled Model Intercomparison Project 5 (CMIP5) GCMs in relation to their skills in simulating mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan using state-of-the-art spatial metrics, SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency, for the period 1961–2005. The multi-model ensemble (MME) precipitation and maximum and minimum temperature data were generated through the intelligent merging of simulated precipitation and maximum and minimum temperature of selected GCMs employing random forest (RF) regression and simple mean (SM) techniques. The results indicated some differences in the ranks of GCMs for different spatial metrics. The overall ranks indicated NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 as the best GCMs in simulating the spatial patterns of mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan. MME precipitation and maximum and minimum temperature generated based on the best-performing GCMs showed more similarities with observed precipitation and maximum and minimum temperature compared to precipitation and maximum and minimum temperature simulated by individual GCMs. The MMEs developed using RF displayed better performance than the MMEs based on SM. Multiple spatial metrics have been used for the first time for selecting GCMs based on their capability to mimic the spatial patterns of annual and seasonal precipitation and maximum and minimum temperature. The approach proposed in the present study can be extended to any number of GCMs and climate variables and applicable to any region for the suitable selection of an ensemble of GCMs to reduce uncertainties in climate projections.