# Dataset - Exploring and Anticipating Extreme East African Short Rains [https://doi.org/10.5061/dryad.f1vhhmh4z](https://doi.org/10.5061/dryad.f1vhhmh4z) This data set contains the time series supporting the main results presented in the upcoming manuscript 'Exploring and Anticipating Extreme East African Short Rains in a Warming World with the Indo-Pacific Heating Gradient'. This deposit contains a spreadsheet with the underlying data to reproduce figures and major results. This data set draws from five widely used sources: * The Climate Hazard Center Infrared Precipitation with Stations archive (CHIRPS) and the Centennial Trends Gridded Rainfall archive: Funk C., Peterson P., Landsfeld M., Pedreros D., Verdin J., Shukla S., Husak G., Rowland J., Hoell A. and Michaelsen J. (2015) The climate hazards group infrared precipitation with stations - a new environmental record for monitoring extremes, Scientific Data, 22, 150066. [http://www.nature.com/articles/sdata201566](http://www.nature.com/articles/sdata201566). doi: 10.1038/sdata.2015.66. Funk C., Nicholson S. E., Landsfeld M., Klotter D., Peterson P. and Harrison L. (2015) The Centennial Trends Greater Horn of Africa Precipitation Dataset, Scientific Data, 2, 150050. DOI: 10.1038/sdata.2015.50. doi:10.1038/sdata.2015.50. * The NOAA Extended Reconstruction sea surface temperature data set (version 5): Boyin Huang, Peter W. Thorne, Viva F. Banzon, Tim Boyer, Gennady Chepurin, Jay H. Lawrimore, Matthew J. Menne, Thomas M. Smith, Russell S. Vose, and Huai-Min Zhang (2017): NOAA Extended Reconstructed Sea Surface Temperature (ERSST), Version 5. NOAA National Centers for Environmental Information. DOI:[10.7289/V5T72FNM](10.7289/V5T72FNM). * ERA5 Reanalysis atmospheric heating values: Hersbach, H., et al. (2017): Complete ERA5 from 1940: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service (C3S) Data Store (CDS). DOI: [10.24381/cds.143582cf](https://doi.org/10.24381/cds.143582cf) * Seasonal SST forecasts from the North American Multi-Model Ensemble (NMME): Kirtman, B. P., et al. (2014). "The North American Multimodel Ensemble: Phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction." Bulletin of the American Meteorological Society 95(4): 585-601. [https://doi.org/10.1175/BAMS-D-12-00050.1](https://doi.org/10.1175/BAMS-D-12-00050.1). [https://www.ncei.noaa.gov/products/weather-climate-models/north-american-multi-model](https://www.ncei.noaa.gov/products/weather-climate-models/north-american-multi-model). While all of these data are publicly available, we compiled this dataset of all the salient time series supporting the basic results in our paper. All data are for October-November-December (OND). **OND Precip Timeseries** Eastern East Africa CHIRPS and Centennial Trends Precipitation Eastern Horn = Kenya, Ethiopia and Somalia east and south of 38E,8N SPI = Standardized Precipitation Index *Eastern Horn Precipitation Time Series* The 'OND Precip Timeseries' tab contains a time series of OND precipitation totals (in mm) for the Eastern Horn of Africa region. Two data sets are provided - 1950-2014 values from the station-based Centennial Trends archive and 1981-2023 observations from the satellite-gauge 'Climate Hazards InfraRed Precipitation with Stations (CHIRPS) archive. The two time-series are very similar, and a regression is used to adjust the 1950-1980 Centennial Trends data with 1981-2023 CHIRPS values. A gamma distribution fit has been used to transform the totals into 'Standardized Precipitation Index' values. **ERA5 Timeseries** The Indo-Warm pool Heating Gradient (IWHG) is based on atmospheric heating over the western Indian Ocean and Indo-Pacific Warm Pool. These time series are provided here, along with total precipitable water. NOAA extended SST v5 and ERA5 OND Data Western Indian Ocean - 50-70E, 10S-10N Indo-Pacific Warm Pool - 90-150E, 15S-15N West IO Atmospheric Heating Anomalies [Wm-2] - Anomalies from 1991-2000 baseline. Warm Pool Atmospheric Heating Anomalies [Wm-2] -Anomalies from 1991-2000 baseline. **NMME Forecasts - SST** NMME sea surface temperature forecasts, for OND, from 7 different lead times, for four ocean regions, are provided here, along with the observed OND sea surface temperatures. * NMME Forecasts-SST(WIO): Western Indian Ocean, 50-70E, 10S-10N * NMME Forecasts-SST(EIO): Eastern Indian Ocean, 90-110E, 10S-0 * NMME Forecasts-SST(EqWestPac): Eq Western Pacific Ocean, 110-140E, 15S-15N * NMME Forecasts-SST(ONDNino34): Nino3.4, 170-120W, 5S-5N **NMME Forecasts - Precipitation** Data drawn from the NMME Precipitation Forecast Multi-Model Means eHorn NMME precipitation for OND, mm per day NMME precipitation forecasts, for the eastern Horn of Africa in OND, from 7 different lead times, are provided, along with the observed Eastern Horn of Africa SPI values. **NMME Forecasts - IWHG** Indo-warm pool Heating Gradient Estimates [Wm-2] Based on a 1982-2023 regression with observed OND IOD, West Pacific and NINO3.4 SST IWHGest = 12 + 323 IOD - 193 WP + 94 Nino3.4 **NMME Forecasts-IWHG(quantile):** Quantile Matched Indo-warm pool Heating Gradient Estimates [Wm-2] NMME-based forecasts OND IWHG values, based on a regression between observed IWHG values and SST in the Indian and Pacific Ocean. This regression translated SST forecasts from the 'NMME Forecasts - SST' tab into IWHG estimates. IHWHG estimates based on the observed OND SST. This correction process translates the estimated IWHG value into a 1982-2023 quanitile, which is then translated into an IWHG value using the observed 1982-2023 ERA5 values. Quantile-matched estimates uses the observed 1982-2023 CHIRPS SPI.

During the 16 rainy seasons since October-November-December (OND) of 2016, the eastern Horn of Africa (eHorn) has experienced an exceptional sequence of extreme rainy seasons, with 8 dry seasons, 6 wet seasons, and just 2 normal rainy seasons (Figure 1). In 2016/17 and 2020/22 climate change-enhanced west Pacific sea surface temperatures (SST) amplified the influence of La Nina, leading to hazard two-season and five-season drought sequences that forced millions of people into starvation as crops failed and millions of livestock perished. Drought conditions during 2020-22 were exceptionally intense, persistent, extensive and hot, devastating livelihoods and producing repetitive, debilitating and cumulative shocks to herds, crops, water availability, and household incomes. More than eight million livestock died and millions of people faced the threat of starvation, and emergency humanitarian relief efforts required more than $2 billion USD. Extreme rains in March-April-May (MAM) of 2018, due to a Madden-Julien Oscillation brought flooding and displacement, while positive Indian Ocean Dipole (IOD) conditions in 2019 and 2023 contributed to excessive rains, flooding and displacement. These extremes provide potential opportunities for prediction, proactive risk management, and improved agricultural and water management outcomes. Here, focusing on OND rains, we explore the use of a new Indo-Pacific Heating Gradient indicator to understand and predict extreme eHorn rains.

This data set draws from five widely used sources: the Climate Hazard Center Infrared Precipitation with Stations archive (CHIRPS), the Centennial Trends Gridded Rainfall archive, the NOAA Extended Reconstruction sea surface temperature data set (version 5), ERA5 Reanalysis atmospheric heating values, and seasonal SST forecasts from the North American Multi-Model Ensemble (NMME). While all of these data are publicly available, we pull together in this dataset all the salient time series supporting the basic results in our paper. All data are for October-November-December (OND).