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Abstract:

Abstract. We introduce Saudi Rainfall (SaRa), a gridded historical and near-real-time precipitation (P) product specifically designed for the Arabian Peninsula, one of the most arid, water-stressed, and data-sparse regions on Earth. The product has an hourly 0.1° resolution spanning 1979 to the present and is continuously updated with a latency of less than 2 h. The algorithm underpinning the product involves 18 machine learning model stacks trained for different combinations of satellite and (re)analysis P products along with several static predictors. As a training target, hourly and daily P observations from gauges in Saudi Arabia (n = 113) and globally (n = 14 256) are used. To evaluate the performance of SaRa, we carried out the most comprehensive evaluation of gridded P products in the region to date, using observations from independent gauges (randomly excluded from training) in Saudi Arabia as a reference (n = 119). Among the 20 evaluated P products, our new product, SaRa, consistently ranked first across all evaluation metrics, including the Kling–Gupta efficiency (KGE), correlation, bias, peak bias, wet-day bias, and critical success index. Notably, SaRa achieved a median KGE – a summary statistic combining correlation, bias, and variability – of 0.36, while widely used non-gauge-based products such as CHIRP, ERA5, GSMaP V8, and IMERG-L V07 achieved values of −0.07, 0.21, −0.13, and −0.39, respectively. SaRa also outperformed four gauge-based products such as CHIRPS V2, CPC Unified, IMERG-F V07, and MSWEP V2.8 which had median KGE values of 0.17, −0.03, 0.29, and 0.20, respectively. Our new P product – available at https://www.gloh2o.org/sara (last access: 24 September 2025) – addresses a crucial need in the Arabian Peninsula, providing a robust and reliable dataset to 91̽�� hydrological modeling, water resource assessments, flood management, and climate research.

Abstract:

<jats:p>Abstract. The Atmospheric Processes And their Role in Climate (APARC) Quasi-Biennial Oscillation initiative (QBOi) has conducted new experiments to explore the modulation of the QBO by El Niño–Southern Oscillation (ENSO). This paper provides an overview of the experimental design and investigates the modulation of the QBO by ENSO using nine climate models used in QBOi. A key finding is a consistent lengthening of the QBO period during La Niña compared to El Niño across all models, aligning with observational evidence. Although several models simulate QBO periods that deviate from the observed mean of approximately 28 months, the relative difference between La Niña and El Niño remains interpretable within each model. The simulated QBO periods during La Niña tend to be longer than those during El Niño, although, in most models, the differences are small compared to that observed. However, the magnitude of this lengthening shows large inter-model differences. By contrast, even the sign of the ENSO effect on QBO amplitude varies among models. Models employing variable parameterized gravity wave sources generally exhibit greater sensitivity of the QBO amplitude to the presence of ENSO than those models using fixed sources. The models capture key observed ENSO-related characteristics, including a weaker Walker circulation and increased equatorial precipitation during El Niño compared to La Niña, as well as a characteristic response in zonal mean zonal wind and temperature. All models also simulate stronger equatorial tropical upwelling in El Niño compared to La Niña up to ∼ 10 hPa, consistent with ERA5 reanalysis. These modulations influence the propagation and filtering of gravity waves. Notably, models with variable parameterized gravity wave sources show stronger wave forcing during El Niño, potentially explaining the shorter QBO period modulation in these models. Further investigation into the complex interplay between ENSO, gravity waves, and the QBO can contribute to improved model formulations. </jats:p>

Abstract:

Seasonal forecasting systems have been operational for over two decades. Here we present a systematic analysis of the performance of operational seasonal forecasting models since their inception. We analyse seasonal forecasting systems from three major international operational centres that have produced and coordinated continuously on operational seasonal forecasts over the past 20 years. Due to the small sample size of available forecasts, it is difficult to draw meaningful conclusions using historical operational forecasts alone, therefore we focus primarily on available model hindcasts. Our analysis, which accounts for differences in ensemble size and period across the forecasting systems, demonstrates that there have been clear improvements in some regions through the different model eras. For both the boreal winter and summer hindcasts, there have been significant improvements in forecasting the tropical regions, which are concurrent with improvements in the skill of tropical sea‐surface temperature (SST) forecasts. These improvements in the Tropics are associated with increased predictability of temperature and precipitation across various continental regions on seasonal timescales. For the extratropics, the picture is more mixed, with strong improvements only evident during the boreal winter season over the North Pacific and North America. The sources of improvement over the winter extratropics are found to be strongly related to improvements in tropical SST skill and related improvements in the strength of the El Niño/Southern Oscillation (ENSO) teleconnection to the Pacific/North America pattern (PNA). Improvements of seasonal forecast skill over the rest of the extratropics, such as over Eurasia, are generally absent or patchy in individual models. The improvements that are found are most pronounced in the newest era models and are broadly associated with improvements in atmospheric model resolution. These improvements in skill are also evident in representative multi‐model ensembles that represent more closely how operational forecasts are used in practice.

Abstract:

While it is widely believed that the intense rainfall in summer 2022 over Pakistan was substantially exacerbated by anthropogenic climate change1, 2, climate models struggled to confirm this3, 4. Using a high-resolution operational seasonal forecasting system that successfully predicted the extreme wet conditions, we perform counterfactual experiments simulating pre-industrial and future conditions. Both experiments also exhibit strong anomalous rainfall, indicating a limited role of CO2-induced forcing. We attribute 10% of the total rainfall to historical increases in CO2 and ocean temperature. However, further increases in the future suggest a weak mean precipitation reduction but with increased variability. By decomposing rainfall and large-scale circulation into CO2 and SST-related signals, we illustrate a tendency for these signals to compensate each other in future scenarios. This suggests that historical CO2 impacts may not reliably predict future responses. Accurately capturing local dynamics is therefore essential for regional climate adaptation planning and for informing loss and damage discussions.

Abstract:

<jats:title>Abstract</jats:title> <jats:p>Flash droughts—characterized by their rapid onset—can cause devastating socioeconomic and agricultural damage. During such events, soil moisture depletion is driven not only by precipitation shortages but also by the elevated atmospheric moisture demand arising due to extreme heat. However, the role of extreme heat in shaping the evolution of flash droughts and their ecological impacts remains uncertain. Here we investigate the processes involved by analysing global reanalysis data from 1950 to 2022. We find that, when flash droughts are accompanied by extreme heat, they exhibit 6.7–90.8% higher severity and 8.3–114.3% longer recovery time than flash droughts without extreme heat. The presence of extreme heat during flash droughts accelerates soil moisture drawdown over high latitudes, where wet soils and enhanced radiation foster evapotranspiration. By contrast, it slows the absolute onset speed in subtropical transitional climate zones owing to evapotranspiration throttling. Our machine learning approach further reveals that hot flash droughts lead to sharper declines in ecosystem productivity, particularly in croplands, thereby threatening global food security. These findings underscore the pressing need for enhanced infrastructure and ecosystem resilience to hot flash droughts in a warming future.</jats:p>