This article examines the off season rainfall in northern coast Tanzania (NCT) including Zanzibar which occurred in January and February 2020 (JF). Like the JF rainfalls of 2001, 2004, 2010, 2016 and 2018, the JF (2020) rainfall was more unique in damages including loss of lives, properties and infrastructures. The study used the NCEP/NCAR reanalysis data to examine the cause of uniqueness of JF rainfall in 2001, 2004, 2010, 2016, 2018 and 2020 over NCT and Zanzibar. These datasets include monthly mean u, v wind at 850, 700, 500, and 200 mb; SSTs, mean sea level pressure (MSLP) anomalies, Dipole Mode Index (DMI), and monthly rainfall from NCT and Zanzibar stations. Datasets were processed and calculated into long term, seasonal, and monthly averages, indeed, Precipitation Index (PI) was calculated. Correlation analysis between the rainfall (December to January), SST, DMI and 850 mb wind vectors; and long-term percentage contribution of investigated parameters was calculated. Results revealed significant positive and negative correlations between JF rainfall, SSTs and DMI. Moreover, JFs of 2004 and 2016 had higher rainfalls of 443 mm with percentage contribution of up to 406%, while January and February, 2020 had the highest of 269.1 and 101.1mm in Zanzibar and 295 and 146.1 mm over and NCT areas, with highest January long-term rainfall contribution of 356% in Zanzibar and 526% over NCT. The DJF (2019/20) had the highest rainfall record of 649.5 mm in Zanzibar contributing up to 286%, while JF 2000 rainfall had a good spatial and temporal distribution over most NCT areas. JF, 2020 rainfall had impacts of more than 20 people died in Lindi and several infrastructures including Kiyegeya Bridge in Morogoro were damaged. Conclusively, more research works on understanding the dynamics of wet and dry JF seasons should be conducted.

Climate change and variability have been inducing a broad spectrum of impacts on the environment and natural resources including groundwater resources. The study aimed at assessing the influence of weather, climate variability, and changes on the quality of groundwater resources in Zanzibar. The study used the climate datasets including rainfall (RF), Maximum and Minimum Temperature (T max and T min ), the records acquired from Tanzania Meteorological Authority (TMA) Zanzibar office for 30 and 10 (2010-2019) years periods. Also, the Zanzibar Water Authority (ZAWA) monthly records of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and Ground Water Temperature (GWT) were used. Interpolation techniques were used for controlling outliers and missing datasets. Indeed, correlation, trend, and time series analyses were used to show the relationship between climate and water quality parameters. However, simple statistical analyses including mean, percentage changes, and contributions to the annual and seasonal mean were calculated. Moreover, t and paired t-tests were used to show the significant changes in the mean of the variables for two defined periods of 2011-2015 and 2016-2020 at p ≤ 0.05. Results revealed that seasonal variability of groundwater quality from March to May (MAM) has shown a significant change in trends ranging from 0.1 to 2.8 mm/L/yr, 0.1 to 2.8 μS/cm/yr, and 0.1 to 2.0˚C/yr for TDS, EC, and GWT, respectively. The changes in climate parameters were 0.1 to 2.4 mm/yr, 0.2 to 1.3˚C/yr and 0.1 to 2.5˚C/yr in RF, T max , and T min , respectively. From October to December (OND) changes in groundwater parameters ranged from 0.2 to 2.5 mm/L/yr

Climate change and variability, has embarked societies in Zanzibar to rely on horticulture (i.e. watermelon production) as an adaptive measure due to an unpromising situation of commonly used agricultural yields. Currently, there is either no or scant information that describes the influence of climate changes and variability to watermelon production in Zanzibar. Thus, this study aimed to determine the influence of climate variability on the quantity of watermelon production in Zanzibar. The study used both primary and secondary datasets, which include the anecdotal information collected from interviewers' responses from four districts of Unguja and Pemba, and climate parameters (rainfall, maximum and minimum temperature (Tmax and Tmin) acquired from Tanzania Meteorological Authority (TMA) at Zanzibar offices. Pearson correlation was used for analyzing the association between watermelon production and climate parameters, while paired t-test was applied to show the significance of the mean differences of watermelon and climate parameters for two periods of 2014-2017 and 2018-2021, respectively. Percentage changes were used to feature the extent to which the two investigated parameters affect each other. The anecdotal responses were sorted, calculated in monthly and seasonal averages, plotted and then analyzed. Results have shown a strong correlation (r = 0.8 at p ≤ 0.02, and r = 0.7) between watermelon production, Tmax and rainfall during OND, especially in Unguja, as well as Tmin during JJA (i.e. r = −0.8 at p ≤ 0.02) in Pemba. Besides, results have shown the existence of significant differences between the means of watermelon production and climate parameter for the two stated periods, indicating that the climate parameters highly affects the watermelon production

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