This study analyzes the potential impacts of stratospheric aerosol injection on extreme precipitation in Senegal, West Africa. Outputs of two global climate models from CMIP5 (HadGEM2-ES and IPSL-CM5A-LR), involved in the GeoMIP G3 experiment, are used and compared with the RCP4.5 and RCP8.5 emission scenarios. Nine extreme precipitation indices recommended by the joint Ccl/WCRP/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI), (R1mm, R10mm, R20mm, RX1DAY, RX5DAY, R95P, R99P, CWD, and CDD) are analyzed during the rainy season June to September (JJAS). First, climate models were evaluated in terms of their capacity to simulate summer extremes precipitation during the historical period (1981-2000), and secondly its changes were examined between the near-term (2030-2049), mid-term (2050-2069), and long-term (2070-2089) relative to the baseline period. Results show that the ensemble mean of the models accurately reproduces the spatial distribution of extremes precipitation in Senegal despite some biases. Historical trends show a significant increase in intense rainfall in the south and a persistence of dry conditions in the northern and eastern parts of the country. Future projections under the RCP4.5 and RCP8.5 scenarios reveal a decrease of the number of rainy days and a shift in the spatial distribution of extreme precipitation toward the southern and southeastern regions of the country. However, the G3 geoengineering experiment significantly mitigates these changes by slowing the decline in the frequency of extreme precipitation over the country. It is therefore essential that policymakers integrate the geoengineering approach into a comprehensive strategy, combining it with ambitious actions to reduce emissions and build resilience to climate change.

A multiple linear regression method and a neural network method are performed to retrieve the Precipitable Water Vapor, surface temperature and relative humidity using microwave (AMSU-A, MHS) and infrared (HIRS) ATOVS sounders. Each method is performed using microwave, infrared, and mixed data separately to assess the best. Near nadir ATOVS data of Dakar region (Senegal) at 12:00 AM and 12:00 PM are used for the whole year 2013. Learning data are from radiosonde and in situ measurements. By comparing them with retrieved data, ECMWF reanalysis data help to validate the different methods. The multiple linear regression method provides good results for microwave data with an RMS of 4.65 mm, 2.27