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Abstract The Climate Hazards Center Coupled Model Intercomparison Project Phase 6 climate projection dataset (CHC-CMIP6) was developed to support the analysis of climate-related hazards, including extreme humid heat and drought conditions, over the recent past and in the near-future. Global daily high resolution (0.05°) grids of the Climate Hazards InfraRed Temperature with Stations temperature product, the Climate Hazards InfraRed Precipitation with Stations precipitation product, and ERA5-derived relative humidity form the basis of the 1983–2016 historical record, from which daily Vapor Pressure Deficits (VPD) and maximum Wet Bulb Globe Temperatures (WBGTmax) were derived. Large CMIP6 ensembles from the Shared Socioeconomic Pathway 2-4.5 and SSP 5-8.5 scenarios were then used to develop high resolution daily 2030 and 2050 ‘delta’ fields. These deltas were used to perturb the historical observations, thereby generating 0.05° 2030 and 2050 projections of daily precipitation, temperature, relative humidity, and derived VPD and WBGTmax. Finally, monthly counts of frequency of extremes for each variable were derived for each time period.

Abstract During and after recent La Niña events, the decline of the eastern East African (EA) March‐April‐May (MAM) rains has set the stage for life‐threatening sequential October‐November‐December (OND) and MAM droughts. The MAM 2022 drought was the driest on record, preceded by three poor rainy seasons, and followed by widespread starvation. Connecting these dry seasons is an interaction between La Niña and climate change. This interaction provides important opportunities for long‐lead prediction and proactive disaster risk management, but needs exploration. Here, for the first time, we use observations, reanalyses, and climate change simulations to show that post‐1997 OND La Niña events are robust precursors of: (a) strong MAM “Western V sea surface temperature Gradients” in the Pacific, which (b) help produce large increases in moisture convergence and atmospheric heating near Indonesia, which in turn produce (c) regional shifts in moisture transports and vertical velocities, which (d) help explain the increased frequency of dry EA MAM rainy seasons. We also show that, at 20‐year time scales, increases in atmospheric heating in the Indo‐Pacific Warm Pool region are attributable to warming Western V SST, which is in turn largely attributable to climate change. As energy builds up in the oceans and atmosphere, during and after La Niña events, we see stronger heating and heat convergence over warm tropical waters near Indonesia. The result of this causal chain is that increased Warm Pool atmospheric heating and moisture convergence sets the stage for dangerous sequential droughts in EA. These factors link EA drying to a stronger Walker Circulation and explain the predictable risks associated with recent La Niña events.

The operational Simplified Surface Energy Balance (SSEBop) model has been utilized to generate gridded evapotranspiration data from Landsat images. These estimates are primarily driven by two sources of information: reference evapotranspiration and Landsat land surface temperature (LST) values. Hence, SSEBop is limited by the availability of Landsat data. Here, in this proof-of-concept paper, we utilize the Continuous Change Detection and Classification (CCDC) algorithm to generate synthetic Landsat data, which are then used as input for SSEBop to generate evapotranspiration estimates for six target areas in the continental United States, representing forests, shrublands, and irrigated agriculture. These synthetic land cover data are then used to generate the LST data required for SSEBop evapotranspiration estimates. The synthetic LST, evaporative fractions, and evapotranspiration data from CCDC closely mirror the phenological cycles in the observed Landsat data. Across the six sites, the median correlation in seasonal LST was 0.79, and the median correlation in seasonal evapotranspiration was 0.8. The median root mean squared error (RMSE) values were 2.82 °C for LST and 0.50 mm/day for actual evapotranspiration. CCDC predictions typically underestimate the average evapotranspiration by less than 1 mm/day. The average performance of the CCDC evaporative fractions, and corresponding evapotranspiration estimates, were much better than the initial LST estimates and, therefore, promising. Future work could include bias correction to improve CCDC’s ability to accurately reproduce synthetic Landsat data during the summer, allowing for more accurate evapotranspiration estimates, and determining the ability of SSEBop to predict regional evapotranspiration at seasonal timescales based on projected land cover change from CCDC.

Abstract This commentary discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision‐making. Following an unprecedented sequence of five droughts, 23 million east Africans faced starvation in 2022, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution‐based insights can be combined with the latest dynamical models to predict droughts at 8‐month lead‐times. We then discuss behavioral and social barriers to forecast use, and review literature examining how EWS might (or might not) enhance agro‐pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization “Early Warning for All” Executive Action Plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limited funding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African‐led EWS, and building better links between EWS and agricultural development efforts can support long‐term adaptation, reducing chronic needs for billions of dollars in reactive assistance. In Africa and beyond, climate change brings increasingly extreme sea surface temperature (SST) gradients. Using climate models, we can often see these extremes coming. Prediction, therefore, offers opportunities for proactive risk management and improved advisory services, if we can create effective societal linkages via cross‐silo collaborations.

Abstract The Southwestern U.S. has been in a megadrought for the past two decades, stressing vegetation across the region and leading to poor forage conditions on rangelands. How has human‐induced climate change affected air temperatures and atmospheric evaporative demand in the greater Four Corners region of the Southwest, and what have the corresponding impacts been to vegetative productivity? We address this question via a multi‐step climate change detection and attribution analysis. We first relate evaporative demand (quantified by Vapor Pressure Deficit, or VPD) and precipitation to the Normalized Difference Vegetation Index (NDVI) in six unique plant‐precipitation zones. VPD is strongly negatively related to NDVI, and the strength of this relation is similar in magnitude to precipitation. We then use a large ensemble of climate change simulations to estimate the magnitude of VPD increases attributable to human‐induced climate change. We find that modest increases in air temperatures have led to large increases in VPD, with the influence of climate change about twice the inter‐annual standard deviation by 2020. Finally, we estimate the reductions in NDVI due to increased VPD and present the reductions in terms of total annual Aboveground Net Primary Productivity. We conclude that human‐induced warming has roughly doubled the magnitude of recent vegetation deficits, especially in mid‐precipitation areas (receiving 250–500 mm of precipitation annually), concentrated in New Mexico. These results indicate that increased temperatures from human‐caused climate change are having persistent and damaging impacts on vegetation productivity, with significant implications for ranchers and other land users in the region.

Measurement(s) volume of hydrological precipitation Technology Type(s) forecast bias correction Sample Characteristic - Organism precipitation Sample Characteristic - Environment climate system Sample Characteristic - Location global

Abstract Until now, driving mechanisms behind recurring droughts and hydroclimate variations that controls the Nile River Basin (NRB) remains poorly understood. Our results show significant hydroclimatic changes that contributed to recent increasing aridity of NRB since the 1970s. Besides climate warming, the influence of stronger ENSO and Indian Ocean dipole (IOD) in NRB has increased after 1980s, which have significantly contributed to NRB’s drought severity at inter-annual to inter-decadal timescales. Our results demonstrate that warming, El Niño and IOD have played a crucial role on NRB’s inter-decadal hydroclimate variability, but IOD has played a more important role in modulating NRB’s hydroclimate at higher timescales than El Niño. Results also indicate that the impacts of positive phases of ENSO and IOD events are larger than the negative phases in the NRB hydroclimate. Further, the southward (westward) shift in stream functions and meridional (zonal) winds caused an enhancement in the blocking pattern, with strong anticyclonic waves of dry air that keeps moving into NRB, has resulted in drier NRB, given stream function, geopotential height and U-wind anomalies associated with El Niño shows that changes in regional atmospheric circulations during more persistent and stronger El Niño has resulted in drier NRB. After 1970s, El Niño, IOD, and drought indices shows significant anti-phase relationships, which again demonstrates that more frequent and severe El Niño and IOD in recent years has led to more severe droughts in NRB. Our results also demonstrate that IOD and and the western pole of the Indian Ocean Dipole (WIO) are better predictors of the Nile flow than El Niño, where its flow has decreased by 13.7 (upstream) and by 114.1 m3/s/decade (downstream) after 1964. In summary, under the combined impact of warming and stronger IOD and El Niño, future droughts of the NRB will worsen.

The overarching goal of this work is to develop and demonstrate methods that support effective agro-pastoral risk management in a changing climate. Disaster mitigation strategies, such as the Sendai Framework for Disaster Risk Reduction (SFDRR), emphasize the need to address underlying causes of disaster risk and to prevent the emergence of new risks. Such assessments can be difficult, because they require transforming changes in meteorological outcomes into sector-specific impact. While it is common to examine trends in seasonal precipitation and precipitation extremes, it is much less common to study how these trends interact with crop and pasture water needs. Here, we show that the Water Requirement (WR) component of the widely used Water Requirement Satisfaction Index (WRSI) can be used to enhance the interpretation of precipitation changes. The WR helps answer a key question: was the amount of rainfall received in a given season enough to satisfy a crop or pasture's water needs? Our first results section focuses on analyzing spatial patterns of climate change. We show how WR values can be used to translate east African rainfall declines into estimates of crop and rangeland water deficits. We also show that increases in WR, during recent droughts, has intensified aridity in arid regions. In addition, using the PWB, we also show that precipitation increases in humid areas of western east Africa have been producing increasingly frequent excessive rainfall seasons. The second portion of our paper focuses on assessing temporal outcomes for a fixed location (Kenya) to support drought-management scenario development. Kenyan rainfall is decreasing and population is increasing. How can we translate this data into actionable information? The United Nations and World Meteorological Organization advise nations to proactively plan for agro-hydrologic shocks by setting aside sufficient grain and financial resources to help buffer inevitable low-crop production years. We show how precipitation, WR, crop statistics, and population data can be used to help guide 1-in-10 and 1-in-25-year low crop yield scenarios, which could be used to guide Kenya's drought management planning and development. The first and second research components share a common objective: using the PWB to translate rainfall data into more actionable information that can inform disaster risk management and development planning.

Many precipitation-driven data products from land data assimilation systems support assessments of droughts, floods, and other societally-relevant land-surface processes. The accumulated precipitation used as input to these products has a significant impact on water budgets; however, the effects of daily distribution of precipitation on these products are not well known. A comparison of the Integrated Multi-satellite Retrievals for GPM (IMERG) and Climate Hazards Group InfraRed Precipitation with Stations version 2 (CHIRPS2) rainfall products over the continental United States (CONUS) was performed to quantify the impacts of the daily distribution of precipitation on biases and errors in soil moisture, runoff, and evapotranspiration (ET). Since the total accumulated precipitation between the IMERG and CHIRPS product differed, a third precipitation product, CHIRPS-to-IMERG (CHtoIM), was produced that used CHIRPS2 accumulated precipitation totals and the daily precipitation frequency distribution of IMERG. This new product supported a controlled analysis of the impact of precipitation frequency distribution on simulated hydrological fields. The CHtoIM had higher occurrences of precipitation in the 0–5 mm day−1 range, with a lower occurrence of dry days, which decreased soil moisture and surface runoff in the land-surface model. The surface soil layer had a tendency to reach saturation more often in the CHIRPS2 simulations, where the number of moderate to heavy precipitation days (>5 mm day−1) was increased. Using the blended CHtoIM product as input reduced errors in surface soil moisture by 5–15% when compared to Soil Moisture Active/Passive (SMAP) data. Similarly, ET errors were also slightly decreased (~2%) when compared to SSEBop data. Moderate changes in daily precipitation distributions had a quantifiable impact on soil moisture, runoff, and ET. These changes usually improved the model when compared to other modeled and observational datasets, but the magnitude of the improvements varied by region and time of year.

Sharing simple ideas across a broad community of practitioners helps them to work together more effectively. For this reason, drought early warning systems spend a considerable effort on describing how hazards are detected and defined. Well-articulated definitions of drought provide a shared basis for collaboration, response planning, and impact mitigation. One very useful measure of agricultural drought stress has been the “Water Requirement Satisfaction Index” (WRSI). In this study, we develop a new, simpler metric of water requirement satisfaction, the Phenological Water Balance (PWB). We describe this metric, compare it to WRSI and yield statistics in a food-insecure region (east Africa), and show how it can be easily combined with analog-based rainfall forecasts to produce end-of-season estimates of growing season water deficits. In dry areas, the simpler PWB metric is very similar to the WRSI. In these regions, we show that the coupling between rainfall deficits and increased reference evapotranspiration amplifies the impacts of droughts. In wet areas, on the other hand, our new metric provides useful information about water excess—seasons that are so wet that they may not be conducive to good agricultural outcomes. Finally, we present a PWB-based forecast example, demonstrating how this framework can be easily used to translate assumptions about seasonal rainfall outcomes into predictions of growing season water deficits. Effective humanitarian relief efforts rely on early projections of these deficits to design and deploy appropriate targeted responses. At present, it is difficult to combine gridded satellite-gauge precipitation forecasts with climate forecasts. Our new metric helps overcome this obstacle. Future extensions could use the water requirement framework to contextualize other water supply indicators, like actual evapotranspiration values derived from satellite observations or hydrologic models.

The mitigation of losses due to extreme climate events and long-term climate adaptation requires climate informed decision-making. In the past few decades, several remote sensing and modeled-based Earth observations (EOs) have been developed to provide an unprecedented global overview and routine monitoring of climate and its impacts on vegetation and hydrologic conditions, with the goal of supporting informed decision-making. However, their usage in decision-making is particularly limited in climate-risk vulnerable and in situ data-scarce regions such as sub-Saharan Africa, due to lack of access to EOs. Here, we describe the Early Warning eXplorer (EWX), which was developed to address this crucial limitation and facilitate the application of EOs in decision-making, particularly in the food and water-insecure regions of the world. First, the EWX's core framework, which includes (i) the Viewer, (ii) GeoEngine, and (iii) Support Applications, is described. Then, a comprehensive overview of the Viewer, which is a web-based interface used to access EOs, is provided. This includes a description of (i) the maps and associated features to access gridded EO data and anomalies for different temporal averaging periods, (ii) time series graphs and associated features to access EOs aggregated over polygons such as administrative boundaries, and (iii) commonly used EOs served by the EWX that provide assessments of climate and vegetation conditions. Next, examples are provided to demonstrate how EWX can be used to monitor development, progression, spatial extent, and severity of climate-driven extreme events to support timely decisions related to mitigation of food insecurity and flooding impacts. Finally, the value of a regional implementation of EWX at the Regional Centre for Mapping of Resources for Development (RCMRD) in Nairobi, Kenya, is highlighted. Regional implementation of the EWX facilitates access to regionally focused EOs and their availability at polygon boundaries most relevant to the local decision-makers. Similar instances of EWX implemented in other regions, especially those susceptible to food and water security, will likely further enhance the application of EOs for informed decision-making.

Since 2015, Sub-Saharan Africa (SSA) has experienced an unprecedented rise in acute food insecurity (AFI), and current projections for the year 2020 indicate that more than 100 million Africans are estimated to receive emergency food assistance. Climate-driven drought is one of the main contributing factors to AFI, and timely and appropriate actions can be taken to mitigate impacts of AFI on lives and livelihoods through early warning systems. To support this goal, we use observations of peak Normalized Difference Vegetation Index (NDVI) as an indicator of seasonal drought conditions following a rainy season to show that delays in the onset of the rainy season (onset date) can be an effective early indicator of seasonal drought conditions. The core of this study is an evaluation of the relationship of the onset dates and peak NDVI, stratified by AFI risks, calculated using AFI reports by the United States Agency of International Development (USAID)-funded Famine Early Warning Systems Network (FEWS NET). Several parts of SSA, mostly located in East Africa (EA), reported the "Crisis" phase of AFI-requiring emergency food assistance-at least one-third of the time between April 2011 to present. The results show that the onset date can effectively explain much of the interannual variability in peak NDVI in the regions with the highest AFI risk level, particularly in EA where the median of correlation (across all the Administrative Unit 2) varies between -0.42 to -0.68. In general, an onset date delay of at least 1 dekad (10 days) increases the likelihood of seasonal drought conditions. In the regions with highest risks of AFI, an onset delay of just 1 dekad doubles the chance of the standardized anomaly of peak NDVI being below -1, making a -1 anomaly the most probable outcome. In those regions, a 2-dekads delay in the onset date is associated with a very high probability (50%) of seasonal drought conditions (-1 standardized anomaly of NDVI). Finally, a multivariate regression analysis between standardized anomaly and onset date anomaly further substantiates the negative impacts of delay in onset date on NDVI anomaly. This relationship is statistically significant over the SSA as a whole, particularly in the EA region. These results imply that the onset date can be used as an additional critical tool to provide alerts of seasonal drought development in the most food-insecure regions of SSA. Early warning systems using onset date as a tool can help trigger effective mid-season responses to save human lives, livestock, and livelihoods, and, therefore, mitigate the adverse impacts of drought hazards.

We examine relationships between the start of rainy season (SOS) and sub-national grain (white maize) market price movements in five African countries. Our work is motivated by three factors: (a) some regions are seeing increasing volatility SOS timing; (b) SOS represents the first observable occurrence in the agricultural season and starts a chain reaction of decisions that influence planting, labor allocation, and harvest—all of which can have direct impacts on local food prices and availability; and (c) pre- and post-harvest price movements provide key insights into supply-and-demand issues related to food insecurity. We start by exploring a number of different SOS definitions using varying reference periods to define whether an SOS is ‘on-time’ or ‘late’. We then compare how those different definitions perform in seasonal price forecasting models. Specifically, we examine if SOS indicators can predict price means over 6 and 9 month periods, or roughly the length of time from planting to market. We use different reference periods for defining ‘early’ versus ‘late’ seasonal starts based on the previous year’s start date, or median start dates over the past 3, 5, and 10 year periods. We then compare the out-of-sample forecast performance of univariate time-series models (autoregressive integrated moving average (ARIMA)) with time-series (ARIMAX) models that include various SOS definitions as exogenous predictors. We find that using some form of SOS indicator (either an SOS anomaly or 1st month’s rainfall anomaly) leads to increased predictive power when examining prices over a 6 months window. However, the results vary considerably by country. We find the strongest performance of SOS indicators in central Ethiopia, southern Kenya, and southern Somalia. We find less evidence in support of the use of SOS indicators for price forecasting in Malawi and Mozambique.

West Africa represents a wide gradient of climates, extending from tropical conditions along the Guinea Coast to the dry deserts of the south Sahara, and it has some of the lowest income, most vulnerable populations on the planet, which increases catastrophic impacts of low and high frequency climate variability. This paper investigates low and high frequency climate variability in West African monthly and seasonal precipitation and reference evapotranspiration from the early 1980s to 2016. We examine the impact of those trends and how they interact with payouts from index insurance products. Understanding low and high frequency variability in precipitation and reference evapotranspiration at these scales can provide insight into trends during periods critical to agricultural performance across the region. For index insurance, it is important to identify low-frequency variability, which can result in radical departures between designed/planned and actual insurance payouts, especially in the later part of a 30-year period, a common climate analysis period. We find that evaporative demand and precipitation are not perfect substitutes for monitoring crop deficits and that there may be space to use both for index insurance design. We also show that low yields—aligned with the need for insurance payouts—can be predicted using classification trees that include both precipitation and reference evapotranspiration.

Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to extreme weather events such as major droughts and floods and thus, an understanding of the strengths and weaknesses of different rainfall products is valuable. Three dekadal (10-day) gridded satellite rainfall products (TAMSAT African Rainfall Climatology And Time-series (TARCAT) v2.0, Famine Early Warning System NETwork (FEWS NET) Rainfall Estimate (RFE) v2.0, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS)) are compared to independent gauge data (2001–2012). This is done using pairwise comparison statistics to evaluate the performance in estimating rainfall amounts and categorical statistics to assess rain-detection capabilities. The analysis was performed for different rainfall categories, over the seasonal cycle and for regions dominated by different weather systems. Overall, satellite products overestimate low and underestimate high dekadal rainfall values. The RFE and CHIRPS products perform as good, generally outperforming TARCAT on the majority of statistical measures of skill. TARCAT detects best the relative frequency of rainfall events, while RFE underestimates and CHIRPS overestimates the rainfall events frequency. Differences in products performance disappear with higher rainfall and all products achieve better results during the wet season. During the cyclone season, CHIRPS shows the best results, while RFE outperforms the other products for lower dekadal rainfall. Products blending thermal infrared and passive microwave imagery perform better than infrared only products and particularly when meteorological patterns are more complex, such as over the coastal, central and south regions of Mozambique, where precipitation is influenced by frontal systems.

There is an active debate regarding the influence that climate has on the risk of armed conflict, which stems from challenges in assembling unbiased datasets, competing hypotheses on the mechanisms of climate influence, and the difficulty of disentangling direct and indirect climate effects. We use gridded historical non-state conflict records, satellite data, and land surface models in a structural equation modeling approach to uncover the direct and indirect effects of climate on violent conflicts in Africa and the Middle East (ME). We show that climate–conflict linkages in these regions are more complex than previously suggested, with multiple mechanisms at work. Warm temperatures and low rainfall direct effects on conflict risk were stronger than indirect effects through food and water supplies. Warming increases the risk of violence in Africa but unexpectedly decreases this risk in the ME. Furthermore, at the country level, warming decreases the risk of violence in most West African countries. Overall, we find a non-linear response of conflict to warming across countries that depends on the local temperature conditions. We further show that magnitude and sign of the effects largely depend on the scale of analysis and geographical context. These results imply that extreme caution should be exerted when attempting to explain or project local climate–conflict relationships based on a single, generalized theory.

Sparse gauge networks in Sub-Saharan Africa (SSA) limit our ability to identify changing precipitation extremes with in situ observations. Given the potential for satellite and satellite-gauge precipitation products to help, we investigate how daily gridded gauge and satellite products compare for seven core climate change precipitation indices. According to a new gauge-only product, the Rainfall Estimates on a Gridded Network (REGEN), there were notable changes in SSA precipitation characteristics between 1950 and 2013 in well-gauged areas. We examine these trends and how these vary for wet, intermediate, and dry areas. For a 31 year period of overlap, we compare REGEN data, other gridded products and three satellite products. Then for 1998–2013, we compare a set of 12 satellite products. Finally, we compare spatial patterns of 1983–2013 trends across all of SSA. Robust 1950–2013 trends indicate that in well-gauged areas extreme events became wetter, particularly in wet areas. Annual totals decreased due to fewer rain days. Between 1983 and 2013 there were positive trends in average precipitation intensity and annual maximum 1 d totals. These trends only represent 15% of SSA, however, and only one tenth of the main wet areas. Unfortunately, gauge and satellite products do not provide consensus for wet area trends. A promising result for identifying regional changes is that numerous satellite products do well at interannual variations in precipitation totals and number of rain days, even as well as some gauge-only products. Products are less accurate for dry spell length and average intensity and least accurate for annual maximum 1 d totals. Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (3B42-V7) and Climate Hazards center Infrared Precipitation with Stations (CHIRPS v2.0) ranked highest for multiple indices. Several products have seemingly unrealistic trends outside of the well-gauged areas that may be due to influence of non-stationary systematic biases. Social media abstract . Sparse data show increasing Africa rainfall extremes and satellite products fill some missing pieces.

This study examines wet season droughts using eight products from the Frequent Rainfall Observations on GridS database. The study begins by evaluating wet season precipitation totals and wet day counts at seasonal and decadal time scales. While we find a high level of agreement among the products at a seasonal time scale, evaluations of 10 year variability indicate substantial non-stationary inter-product differences that make the assessment of low-frequency changes difficult, especially in data-sparse regions. Some products, however, appear more reliable than others on decadal time scales. Global time series of dry, middle, and wet region standardized precipitation index time series indicate little coherent change. There is substantial coherence in year-to-year variations in these time series for the better-performing products, likely indicative of skill for monitoring variations at large spatial scales. During the wet season, the data do not appear to indicate widespread global changes in precipitation, reference evapotranspiration (RefET) or Standardized Precipitation Evapotranspiration Index (SPEI) values. These data also do not indicate a global shift towards increasing aridity. Focusing on SPEI values for dry regions during droughts, however, we find modest increases in RefET and decreases in SPEI when wet season precipitation is below normal. Dry region SPEI values during droughts have decreased by −0.2 since the 1990s. The cause of these RefET increases is unclear, and more detailed analysis will be needed to confirm these results. For wet regions, however, the majority of products appear to indicate increases in wet season precipitation, although many products perform poorly in these regions due to limited observation networks, and estimated increases vary substantially. Synopsis: Our analysis indicates a lack of increasing aridity at global scales, issues associated with non-stationary systematic errors, and concerns associated with increases in reference evapotranspiration in global dry regions during droughts.

While there are obstacles to the exchange of long-term high temporal resolution precipitation data, there have been fewer barriers to the exchange of so-called ‘indices’. These are derived from daily and sub-daily data and measure aspects of precipitation frequency, duration and intensity that could be used for the study of extremes. This paper outlines the history of the rationale and use of these indices, the types of indices that are frequently used and the advantages and pitfalls in analysing them. Moving forward, satellite precipitation products are now showing the potential to provide global climate indices to supplement existing products using longer-term in situ gauge records but we suggest that to advance this area differences between data products, limitations in satellite-based estimation processes, and the inherent challenges of scale need to be better understood.

In East Africa, accurate grain yield predictions can help save lives and protect livelihoods. Regional grain yield forecasts can inform decisions regarding the availability and prices of key staples, food aid, and large humanitarian responses. Here, we use earth observation (EO) products to develop and evaluate subnational grain yield forecasts for 56 regions located in two severely food insecure countries: Kenya and Somalia. We identify, for a given region and time of year, which, if any, product is the best indicator for end-of-season maize yields. Our analysis seeks to inform a real-world situation in which analysts have access to multiple regularly updated EO data products, but predictive skill corresponding to each may vary across these regions and throughout the season. We find that the most accurate predictions can be made for high-producing areas, but that the relationship between production and forecast accuracy diminishes in areas with yields averaging greater than one metric ton per hectare. However, while forecast accuracy is highest in high production areas, in many of these regions, the forecast accuracy of models using EO products is not better than a set of baseline models that do not use EO products. Overall, we find that rainfall is the best indicator in low-producing regions and that other EO products work best in areas where yields are relatively consistent, but production is still limited by environmental factors.

December–February precipitation in southern Africa during recent El Niño events is studied by distinguishing circulation and precipitation responses during strong and moderate-to-weak events. We find that while both strong and moderate-to-weak El Niño events tend to dry southern Africa, the pattern and magnitude of precipitation anomalies in the region are different, with strong El Niño events resulting in rainfall deficits often less than −0.88 standardized units and deficits of only about half that for the moderate-to-weak case. Additionally, the likelihood of southern Africa receiving less than climatologic precipitation is approximately 80% for strong El Niño events compared to just over 60% for moderate-to-weak El Niño. Strong El Niño events are found to substantially disrupt onshore moisture transports from the Indian Ocean and increase geopotential heights within the Angola Low. Since El Niño is the most predictable component of the climate system that influences southern Africa precipitation, the information provided by this assessment of the likelihood of dry conditions can serve to benefit early warning systems.