Abstract Understanding changes in intraseasonal rainfall variability is critical for improving sub-seasonal prediction. While the amplification of intraseasonal rainfall variability across Australia has been reported, the spatial heterogeneity of this intensification has been overlooked. Here, we observe that the intraseasonal rainfall variability has been intensified, particularly over northeastern Australia during the wet season (November–April) over the past four decades. The intensified intraseasonal rainfall variability is primarily attributed to enhanced intensity of precipitation events, rather than drier spells. Moisture budget analysis indicates that the intensification of intraseasonal abnormal rainfall events over northeastern Australia is primarily due to the strengthened vertical advection of background moisture caused by the intraseasonal vertical motion anomalies. In this process, the strengthening of intraseasonal vertical velocity driven by changes in intraseasonal wind convergence plays a dominant role, accounting for over 90%, whereas the effect of background moisture changes is relatively less significant. Our findings highlight northeastern Australia as a hotspot for amplified intraseasonal rainfall variability, rather than the whole of Australia. This study provides fundamental insights into northern Australia’s changing climate, and highlights the important role of intraseasonal circulation on northeastern Australia rainfall variability.

Since last century, Sikkim has witnessed substantial climatic fluctuations that can affect the local water availability, agricultural productivity, and livelihood security. Despite this growing concern, a limited study incorporated century-long datasets to explore the underlying climatic pattern of the state and often focuses only on trend analysis without applying a diverse set of statistical and spatial techniques. Hence, a comprehensive analysis has been employed to explore the long-term trend and variability of Sikkim’s yearly and seasonal precipitation patterns from 1923 to 2023.Several statistical matrices have been applied viz. Coefficient of Variation (CV), Analysis of Variance (ANOVA) and Tukey’s Honest Significant Difference (HSD) Test to analyze the intra-seasonal and inter-seasonal rainfall variability, Standardized Precipitation Index (SPI) has been used to find out wet and dry years, Precipitation Concentration Index (PCI) shows annual and seasonal rainfall distribution, Simple Moving Average method has been applied to detect long-term trend, Inverse Distance Weighted interpolation technique visualizes the spatial rainfall variation in the state. This research offers new insights into the precipitation behavior and shifting rainfall dynamics such as summer rainfall demonstrates the most significant inter-cluster difference, wet and dry events are significantly increasing in summer season, the highest variability in precipitation distribution can be observed in monsoon rain, the summer and winter rainfall is increasing, where monsoon rainfall is decreasing significantly over the region, monsoon rainfall shows significant increasing trend in Gyalshing and Mangan, where summer and winter rain is increasing at Gyalshing and Mangan. These findings can help in regional adaptation strategies, water resource management, agricultural planning, and minimize climate-related livelihood vulnerabilities, as most of the farmers of this state rely on rainfall for their irrigation purposes.

Background and purpose: Increasing intra-seasonal rainfall variability poses a major challenge to the sustainable intensification of rainfed maize systems in sub-Saharan Africa. This study investigates how intra-seasonal rainfall patterns and extreme dry and wet events affect maize productivity and nitrogen (N) use, particularly under crop residue mulching—a practice widely promoted to improve soil water and N availability. Methods: A maize field experiment with manipulated rainfall conditions was conducted over two cropping seasons (2022–23 and 2023–24) in sub-humid Zimbabwe. The factorial design combined three rainfall treatments (ambient, 30 % reduced rainfall, and heavy rainfall with two additional artificial events of 100 mm day−1 each), with or without mulch (0 vs. 6 t DM ha−1) and N fertilization (0 vs. 80 kg N ha−1). Measured variables included aboveground biomass, plant N accumulation, grain yield, yield components, and harvest indices. The relative influence of rainfall variability and management practices was assessed. Results: The two seasons showed contrasting rainfall: 2022–23 was near-normal, while 2023–24 (an El Niño year) was drier, with uneven rainfall distribution. Intra-seasonal rainfall patterns and extremes explained 78 % of maize yield variability. Poor rainfall distribution significantly decreased maize productivity and N use, despite adequate total seasonal rainfall. Rainfall reduction decreased yield by 22 % in 2022–23 but increased it by 20 % in 2023–24. Heavy rainfall, especially with N fertilization, doubled grain yield in 2023–24. Mulching provided no buffering effect and reduced maize biomass and N uptake by about one-third in 2023–24. Conclusions: Intra-seasonal rainfall patterns and extremes were the dominant factors affecting maize productivity and N use, far outweighing the effects of mulch and N fertilization. These findings highlight the need for cropping strategies that better account for intra-seasonal rainfall variability to improve the resilience and sustainability of rainfed maize systems in sub-Saharan Africa.

Semi-arid zones are becoming more susceptible to the impact of the alteration of rainfalls which are other significant challenges to the resilience of agriculture and their food security. The authors compared the experience of three decades (1990-2020) of climatic and agricultural data in the Sahel, East Africa, the Deccan Plateau of India, and Western Australia to describe how changes in rainfall timing, intensity, and variability affect crop productivity and stability in food systems. In a mixed methodology, the timing of rainfall onset, stop early in the season, the frequency of intra-season drought, and intra-seasonal rainfall concentration indices were analyzed along with crop yield of millet, sorghum, maize, and wheat and regional food security indices. It was found that the effects of intra-seasonal dry spells and delayed onset of rain during a season are much stronger negative factors towards crop yields compared to their total annual rainfall and sorghum in India and maize in East Africa were especially susceptible to variability in rainfall distribution. Regression and correlation analysis also supported the fact that dry spell frequency was the most important factor that causes fall in yield and it was the main contributor of variability in the yield of about 60 percent in some regions. The anomalies in rainfall were directly related to the levels of crop failure and increased incidences of food insecurity especially in the Sahel and East Africa which is where the volatility of markets and smaller adaptive capacities worsens exposure. On the other hand, Western Australia exhibited partial resilience as a result of conservation agriculture using machines and policy propagation. Findings highlight the dire necessity of climate-resilient crop adaptation, water-harvesting and soil conservation practices and institutional interventions like climate information services and crop insurance in protecting food system in semi-arid zones characterized by rising climate variability. Keywords: Semi-arid regions; rainfall variability; agricultural resilience; intra-seasonal dry spells; crop yields; food security; rainfall onset; climate change adaptation; drought-tolerant crops; soil and water conservation.

The El Niño–Southern Oscillation (ENSO) significantly affects climate extremes, particularly causing the driest conditions across the Indonesian Maritime Continent (IMC). However, the specific impacts of the 2023 ENSO on weather pattern anomalies in the IMC have not been thoroughly explored. This study examines the 2023 El Niño, one of the strongest El Niño conditions during the warmest climate decade, and its effects on weather anomalies (i.e. hot and humid) to fill gaps in our understanding of the diverse impacts of ENSO in the IMC. Composite analysis (1991–2023) from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) and National Oceanic and Atmospheric Administration datasets demonstrated that the 2023 El Niño strengthened westerly winds across the Pacific Ocean and influenced intraseasonal rainfall patterns over the IMC. Distinct contrasts were observed between northern (DOM1) and southern (DOM2) IMC. During the onset and mature periods of El Niño (April–September 2023), DOM2 experienced reduced rainfall and significant drought, especially during the dry season (June–September 2023), affecting vital agricultural regions in Java and southern Sumatra. The positive Indian Ocean Dipole and vigorous Australian monsoon likely intensified this drought. Conversely, DOM1 experienced increased rainfall, triggering severe flood events in several regions in Sumatra and Kalimantan (i.e. northern Sumatra and western, northern and central Kalimantan). Our findings highlighted that the enhanced rainfall, driven by middle and high cloud activities, is linked to ENSO and South China Sea warming. This study deepens our understanding of the varied impacts of El Niño on intraseasonal weather patterns in Indonesia, ultimately aiding in improving sub-seasonal-to-seasonal climate predictions over the IMC.

Malnutrition, particularly its impact on child morbidity and mortality, is one of the top five health effects of climate change. However, quantifying the portion of malnutrition attributed to climate remains challenging due to various confounding factors. This study examines the relationship between climate and acute malnutrition in Niger, a country highly vulnerable to climate change and disasters. Since climate’s effect on malnutrition is indirect, mediated by crop production, we combine rainfall data from TAMSAT satellite estimates with the SARRA-O crop model, which simulates the impact of rainfall variability on crop yields. Our analysis reveals a significant correlation between malnutrition and both rainfall and crop production from the previous year, but not within the same year. The strongest correlation (R = −0.72) was found with the previous year’s crop production. No significant links were found with temperature or intra-seasonal rainfall indices, like the start or duration of the rainy season. Although national correlations between global malnutrition, rainfall, and crop yields were stronger, they were weaker or absent at the regional level and, for Severe Acute Malnutrition crises, are less likely driven by climate variability. However, the one-year lag in the correlation allows for the prediction of future food crises, providing an opportunity to implement early intervention measures.

Abstract This study introduces four univariate regional indices to improve the representation of intraseasonal rainfall variability across South America throughout the year, focusing on Brazil. These indices are constructed using two distinct approaches: the linear Empirical Orthogonal Functions (EOF) method and the unsupervised machine‐learning Self‐Organizing Maps (SOM) technique. Both methods are applied to Outgoing Longwave Radiation (OLR) and precipitation‐filtered anomalies in the 30–90‐day band over the South American domain. Results demonstrate that regional indices provide valuable insights into intraseasonal South American rainfall variability, including phase and strength, compared to global indices of the Madden‐Julian Oscillation (MJO). Despite being computed using only the South American domain, the regional indices capture the tropical‐tropical MJO teleconnection through the zonal wavenumber‐1 structure. The diversity in amplitude and evolution of precipitation, primarily influenced by tropical‐extratropical teleconnections through Rossby wave trains, is more evident when using the non‐linear SOM index. The regional indices also accurately measure the impacts of intraseasonal variability on extreme precipitation events over Brazil. Case studies, such as the 2013/2014 summer drought episode, highlight this ability, when a deficient rainy season severely affected the Southeast Region of Brazil, impacting agricultural production and hydroelectric power generation. During this episode, the regional indices show agreement between drought periods and suppressed precipitation phases, while global indices indicate an inactive MJO phase. These findings underscore the effectiveness of regional indices in capturing intraseasonal variability, offering significant implications for extreme weather prediction and their impacts on South American water resources and socio‐economic activities.

Abstract Monsoonal precipitation is dominated by intraseasonal variabilities, whose skillful prediction lead time is currently less than 5 days and remains a grand challenge. Here we show that an intrinsic variability in the Indian Ocean, the Central Indian Ocean (CIO) mode, when combined with a machine learning (ML) algorithm, can produce skillful predictions of intraseasonal precipitation over the monsoon region with a lead time of over 15 days, which is close to the theoretical predictability limit. This remarkable skill improvement stems from the fact that the CIO mode is dynamically related to the intraseasonal monsoon rainfall, while the data‐driven ML algorithm suppresses unwanted high‐frequency noise. Using the CIO mode and the ML algorithm, the forecast system hybridizes physical fundamentals and versatility of data‐driven algorithms. The identification of CIO mode and the verification of its significant contribution to intraseasonal predictions advance our understanding of the coupled monsoon system and also underscores the great potential of ML techniques in weather forecasts and climate predictions.

Abstract This paper investigates the relationship between the intraseasonal oscillation (ISO) and rainfall patterns in Central Africa during the March-April-May (MAM) season. Using CHIRPS and TAMSAT precipitation data from 1983 to 2019, we analyzed the inter-annual variability of ISO spatial structure and its impact on rainfall and extreme rainfall indices. Empirical Orthogonal Function (EOF) analysis classified years into positive (10 years), negative (10 years), mixed (6 years), and neutral (11 years) ISO types. Composite rainfall anomalies were constructed based on these classifications. Results revealed significant inter-annual rainfall variability, with distinct spatial patterns associated with positive and negative ISO years. A significant spatial correlation (over 0.4) was found between ISO variations and rainfall, particularly in the eastern region. Analysis of the impact rate of ISO years showed a more nuanced distribution in CHIRPS data compared to TAMSAT. Extreme rainfall indices, calculated using ETCCDI methods, exhibited spatial disparities, with dry zones in the north and south contrasting with wetter coastal areas and Lake Victoria. Composite extreme rainfall index anomalies based on positive and negative ISO years demonstrated varying influences depending on the region and index. Positive ISO years generally saw a decrease in consecutive dry days (CDD) and an increase in consecutive wet days (CWD), extreme rainfall intensity (RR1, RR20, R95ptot, SDII) along the Atlantic coast and northwestern Ethiopia. Neutral ISO years often displayed opposite trends to mixed years, except for the RR1 index. Understanding these relationships is crucial for water resource management in Central Africa, enabling better forecasting and mitigation of extreme rainfall events.

Abstract A multichannel singular spectrum analysis of 121 years of daily rainfall over India and 43 years of outgoing longwave radiation and horizontal wind has revealed two dominant intraseasonal oscillations (ISOs) that describe the space–time variability of monsoon rainfall. The two nonlinear oscillations are not perfectly periodic but exhibit broadband spectra centered at 45 and 28 days. These modes have coherent and near-regular spatial structure and temporal variations. This study posits that the two nonlinear oscillations provide a consistent and comprehensive framework to study the variability and predictability of intraseasonal rainfall variations. The active and break cycles of monsoon rainfall over India have been traditionally defined by using an arbitrarily chosen duration of persistence of arbitrarily chosen amount of rainfall averaged over an arbitrarily chosen area. This study shows that the phases of the two objectively derived modes of variability provide an objective method for defining the active and break cycles. The two ISOs are further shown to make negligible contribution to the seasonal mean rainfall and its interannual variability. This study proposes that the investigations of the origins of these nonlinear modes, and modulations of their amplitudes and phases, provide a new paradigm for future research on validation and predictability of intraseasonal variations in climate models.

Agriculture in Ethiopia is highly dictated by spatial patterns and temporal distributions of climate variables. The analysis of these climate variables is crucial for understanding the impacts on agricultural productivity. This study aimed to analyze spatiotemporal variability and trends of intra-seasonal rainfall and temperature using site-specific daily data from the Ethiopian Meteorology Institute (1992–2021). Standardized methods explore variability, while Mann–Kendall tests identify trends, using the Modified version for data with autocorrelation. Inverse Distance Weighted interpolation was employed for spatial analysis of rainfall, length of growing season, and temperature. The findings identified that Kiremt dominated the mono-modal rainfall pattern, contributing 72%-86% of total annual rainfall. The study found that the season typically begins early on June 13 in Adiszemen, and July 6 in Arbgebiya and ends between October 6 and October 26. The duration of the season varied across locations, averaging 95 days at Ebenat and 148 days at Adiszemen. The seasonal rainfall anomaly index shows identical patterns between ENSO episodes and seasonal rainfall. These findings inform decision-making and adaptation strategies for ENSO-driven rainfall variability. Temperatures showed predictable seasonal patterns, but have significantly increased over time, with maximum and minimum temperatures rising 0.014 °C to 0.421 °C and 0.027 °C to 0.485 °C per year respectively. This warming trend is negatively impacting water, crops, and livestock, requiring adaptation measures to build regional resilience. This study underscores the critical impact of climate variability on agriculture in the study area. The findings reveal shifts in rainfall patterns and temperature trends, providing essential insights for adapting agricultural practices.

Abstract. Correctly representing the response of vegetation productivity to water availability in Earth system models (ESMs) is essential for accurately modelling the terrestrial carbon cycle and the evolution of the climate system. Previous studies evaluating gross primary productivity (GPP) in ESMs have focused on annual mean GPP and interannual variability, but physical processes at shorter timescales are important for determining vegetation–climate coupling. We evaluate GPP responses at the intraseasonal timescale in five CMIP6 ESMs by analysing changes in GPP after intraseasonal rainfall events with a timescale of approximately 25 d. We compare these responses to those found in a range of observation-based products. When composited around all intraseasonal rainfall events globally, both the amplitude and the timing of the GPP response show large inter-model differences, demonstrating discrepancies between models in their representation of water–carbon coupling processes. However, the responses calculated from the observational datasets also vary considerably, making it challenging to assess the realism of the modelled GPP responses. The models correctly capture the fact that larger increases in GPP at the regional scale are associated with larger increases in surface soil moisture and larger decreases in atmospheric vapour pressure deficit. However, the sensitivity of the GPP response to these drivers varies between models. The GPP in NorESM is insufficiently sensitive to vapour pressure deficit perturbations when compared all to other models and six out of seven observational GPP products tested. Most models produce a faster GPP response where the surface soil moisture perturbation is larger, but the observational evidence for this relationship is weak. This work demonstrates the need for a better understanding of the uncertainties in the representation of water–vegetation relationships in ESMs and highlights a requirement for future daily-resolution observations of GPP to provide a tighter constraint on global water–carbon coupling processes.

Abstract Remote influences on intraseasonal anomalous rainfall over regions that encompass North and South Vietnam are explored using a 38‐year (1979–2016) global dataset over the extended summer (May–October). Wet and Dry composites in filtered daily data with lags of up to 2 weeks are assembled for various rainfall indices over the two subregions, including atmospheric reanalysis products and in‐situ rainfall data. On the regional scale, the moisture flux convergence correlates well with reanalysed rainfall. The large‐scale dynamics associated with these composites are described. Rainfall composites of opposing signs show asymmetrical large‐scale precursors and different pathways of influence. Wet and Dry anomalies in North Vietnam are seen to originate from Europe and propagate at high latitudes. The exact nature of the precursors is sensitive to the definition of the composite index. There is also a pathway of influence along the Asian jet, which impacts South Vietnam, especially for Wet composites which often coincide with Dry conditions in the North. South Vietnam is also influenced by tropical divergent precursors, which are again asymmetric between Wet and Dry composites.

Role of local absorbing aerosols in modulating Indian summer monsoon rainfall

Abstract During boreal winter (December–February), the South American monsoon system (SAMS) reaches its annual maximum when upper‐tropospheric westerly winds prevail over the equatorial Atlantic. Atmospheric dynamic model simulations suggest that Rossby waves generated over South America can propagate to and potentially influence weather patterns in the Northern Hemisphere (NH). However, observational evidence has been absent previously. Here we focus on southeastern South American (SESA) precipitation anomalies, which can characterize intraseasonal rainfall variability of the SAMS. Since tropical “westerly duct” and convective heating are important factors for cross‐equatorial propagation of Rossby wave (CEPRW), we identify two groups of events based on the two factors. By comparing the events associated with both SESA rainfall and tropical westerlies to the events associated with tropical westerlies only, we find that an anomalous Rossby wave train is triggered by precipitation anomalies over SESA, propagates in the southwest–northeast direction, and subsequently crosses the equator. Over a period of 4 days, near‐surface temperature over northwestern Africa and western Europe becomes warmer, accompanied by increased surface downward longwave radiation and precipitable water. The equatorward propagating Eliassen–Palm flux anomalies originated from SESA support the evidence of CEPRW. Simulations using a time‐dependent linear barotropic model forced by prescribed divergence anomalies over SESA further confirm that SESA rainfall can influence the NH weather patterns through CEPRW. Knowledge of this study will help us better understand and model interhemispheric teleconnections over the American–Atlantic–African/European sector.

West African Sahel is one of the most exposed areas to the adverse effects of climate variability.All the agricultural production systems are affected, exposing local populations to food insecurity and poverty.This study aimed to assess the impacts of intra-seasonal rainfall variability and cropping practices on cereal yields in the North Central region of Burkina Faso.Daily rainfall data covering the 1984-2015 period were collected from eleven stations across the region.The agro-climatic parameters such as the onset and the end of the rainy season, its length, seasonal rainfall amount, rainy days and long dry spells in the rainy months were determined.Annual cereal yields statistics (sorghum, millet and maize) were used.Data on cropping practices were taken into account in this study.The statistical methods for trends and breaks were applied to data series.Simple correlation tests were used to assess the impacts of agro-climatic parameters on cereal yields.The results showed that the North Central region of Burkina Faso experienced extreme rainfall events such as "false starts" of rainy seasons, long dry spells and early rainfall cessation.The onset of the rainy season and the long dry spells in July (duration ≥ 8 days) and August (≥ 6 days) months had negative impacts on cereal yields in the region.The results also highlighted an increase in rainfall since the 1990s and 2000s.Increased rainfall and the positive effects of changes in cropping practices affected cereal yields, which increased significantly (44 to 72%) since that period.Dissemination of climate information, adoption of improved cropping technics and supplemental irrigation are innovating practices that could increase cereal yields in North Central Burkina Faso.

Intra-seasonal rainfall variability and crop yield in the Upper East Region of Ghana

Abstract East Africa is particularly vulnerable to weather extremes, with severe weather linked to thousands of deaths per year. Improved forecasts of convective events in this region are urgently needed, from both nowcasting and numerical weather prediction models. Improving these forecasts requires further knowledge of convection in this region. This study aims to improve understanding of convective events in East Africa, based on a six‐year climatology of convective life cycles and the associated precipitation. Convective systems are identified as contiguous areas of cold cloud in geostationary satellite measurements over East Africa. A tracking algorithm is used to trace the evolution of the properties of these systems through time and space. Matching the systems to surface precipitation obtained from satellite microwave observations provides insight into how the life cycles of these systems relate to precipitation at the surface. Over the region as a whole, 59% of the accumulated precipitation can be attributed to the tracked convective systems. The majority (81%) of heavy precipitation events (10 mmhr−1) are attributable to convective systems, while light rainfall events (1 mmhr−1) are not (1.8%). Most of the tracked convective systems have an area less than 400 km2 and last for less than an hour. However, the less frequent larger longer‐lived systems produce the majority of the regional accumulated precipitation. Composite life cycles of the tracked systems show rapid intensification and the heaviest precipitation initially, followed by a steady increase in area and weakening in intensity before the system decays. Finally, the Madden–Julian oscillation, which plays a key role in intraseasonal rainfall variability in the region, is also linked to the amount of rainfall due to convective systems through changes in the frequency and properties of these systems.

Abstract In semi‐arid environments, rainfall‐driven soil moisture fluctuations exert a strong influence on surface turbulent fluxes. Intraseasonal rainfall variability can therefore impact low‐level atmospheric temperatures and influence regional circulations. Using satellite observations and an atmospheric reanalysis, we investigate whether rainfall variability induced by the Madden–Julian oscillation (MJO) triggers land–atmosphere feedbacks across East Africa.We identify that surface fluxes during the East African wet seasons (March–May and October–December) are sensitive to MJO‐induced precipitation variations across low‐lying regions of South Sudan and highland regions of Uganda and southwest Kenya. For example, during MJO phases 6 to 8, when rainfall is suppressed, surface temperatures and sensible heat fluxes increase, whilst evapotranspiration decreases. Spatial variations in the surface flux response to rainfall variability feed back onto the atmosphere through amplifying MJO‐associated pressure anomalies. During dry MJO events for instance, surface warming across the exit region of the Turkana channel increases the low‐tropospheric along‐channel pressure gradient and intensifies the Turkana jet. We conclude that average surface‐driven temperature fluctuations during a single day are responsible for approximately 12% of MJO‐associated variability of the Turkana jet speed. However, we expect that the accumulation of heat over multiple days to the west of the East African Highlands further amplifies anomalies in the pressure gradient and jet intensity. Modelling experiments are required to quantify the accumulated impact of the surface forcing. Surface‐driven Turkana jet variations influence the East African moisture budget and affect the intensity and inland propagation of coastal convection. Not only is this the first study to investigate the importance of intraseasonal land–atmosphere feedbacks across East Africa, but it is also the first to show that Turkana jet characteristics are partly driven by surface conditions. This work motivates an investigation into whether subseasonal forecast models fully harness the predictability from surface‐induced jet variations.

Role of tropical-extratropical interactions in the unprecedented 2022 extreme rainfall in Pakistan: A historical perspective